references.bib

@inproceedings{mcgarry_identifying_2015,
	title = {Identifying candidate drugs for repositioning by graph based modeling techniques based on drug side-effects},
	author = {Mcgarry, Kenneth and Slater, Nicole and Amanning, Angela},
	year = {2015}
}

@article{sheikh_gat2vec:_2018,
	title = {gat2vec: representation learning for attributed graphs},
	issn = {0010-485X, 1436-5057},
	shorttitle = {gat2vec},
	url = {http://link.springer.com/10.1007/s00607-018-0622-9},
	doi = {10.1007/s00607-018-0622-9},
	language = {en},
	urldate = {2019-02-17},
	journal = {Computing},
	author = {Sheikh, Nasrullah and Kefato, Zekarias and Montresor, Alberto},
	month = apr,
	year = {2018}
}

@article{su_network_2018,
	title = {Network embedding in biomedical data science},
	issn = {1467-5463, 1477-4054},
	url = {https://academic.oup.com/bib/advance-article/doi/10.1093/bib/bby117/5228144},
	doi = {10.1093/bib/bby117},
	language = {en},
	urldate = {2019-02-17},
	journal = {Briefings in Bioinformatics},
	author = {Su, Chang and Tong, Jie and Zhu, Yongjun and Cui, Peng and Wang, Fei},
	month = dec,
	year = {2018}
}

@article{ali_biokeen:_2018,
	title = {{BioKEEN}: {A} library for learning and evaluating biological knowledge graph embeddings:},
	shorttitle = {{BioKEEN}},
	url = {http://biorxiv.org/lookup/doi/10.1101/475202},
	doi = {10.1101/475202},
	abstract = {Knowledge graph embeddings (KGEs) have received significant attention in other domains due to their ability to predict links and create dense representations for graphs' nodes and edges. However, the software ecosystem for their application to bioinformatics remains limited and inaccessible for users without expertise in programming and machine learning. Therefore, we developed BioKEEN (Biological KnowlEdge EmbeddiNgs) and PyKEEN (Python KnowlEdge EmbeddiNgs) to facilitate their easy use through an interactive command line interface. Finally, we present a case study in which we used a novel biological pathway mapping resource to predict links that represent pathway crosstalks and hierarchies.

Availability: BioKEEN and PyKEEN are open source Python packages publicly available under the MIT License at https://github.com/SmartDataAnalytics/BioKEEN and https://github.com/SmartDataAnalytics/PyKEEN as well as through PyPI.},
	urldate = {2019-02-17},
	journal = {bioRxiv},
	author = {Ali, Mehdi and Hoyt, Charles Tapley and Domingo-Fernandez, Daniel and Lehmann, Jens and Jabeen, Hajira},
	month = nov,
	year = {2018}
}

@article{cui_survey_2017,
	title = {A {Survey} on {Network} {Embedding}},
	url = {http://arxiv.org/abs/1711.08752},
	abstract = {Network embedding assigns nodes in a network to low-dimensional representations and effectively preserves the network structure. Recently, a significant amount of progresses have been made toward this emerging network analysis paradigm. In this survey, we focus on categorizing and then reviewing the current development on network embedding methods, and point out its future research directions. We first summarize the motivation of network embedding. We discuss the classical graph embedding algorithms and their relationship with network embedding. Afterwards and primarily, we provide a comprehensive overview of a large number of network embedding methods in a systematic manner, covering the structure- and property-preserving network embedding methods, the network embedding methods with side information and the advanced information preserving network embedding methods. Moreover, several evaluation approaches for network embedding and some useful online resources, including the network data sets and softwares, are reviewed, too. Finally, we discuss the framework of exploiting these network embedding methods to build an effective system and point out some potential future directions.},
	urldate = {2019-02-17},
	journal = {arXiv:1711.08752 [cs]},
	author = {Cui, Peng and Wang, Xiao and Pei, Jian and Zhu, Wenwu},
	month = nov,
	year = {2017},
	note = {arXiv: 1711.08752},
	keywords = {Computer Science - Social and Information Networks},
}

@inproceedings{grover_node2vec:_2016,
	address = {San Francisco, California, USA},
	title = {node2vec: {Scalable} {Feature} {Learning} for {Networks}},
	isbn = {978-1-4503-4232-2},
	shorttitle = {node2vec},
	url = {http://dl.acm.org/citation.cfm?doid=2939672.2939754},
	doi = {10.1145/2939672.2939754},
	language = {en},
	urldate = {2019-02-17},
	booktitle = {Proceedings of the 22nd {ACM} {SIGKDD} {International} {Conference} on {Knowledge} {Discovery} and {Data} {Mining} - {KDD} '16},
	publisher = {ACM Press},
	author = {Grover, Aditya and Leskovec, Jure},
	year = {2016},
	pages = {855--864}
}

@inproceedings{tang_line:_2015,
	address = {Florence, Italy},
	title = {{LINE}: {Large}-scale {Information} {Network} {Embedding}},
	isbn = {978-1-4503-3469-3},
	shorttitle = {{LINE}},
	url = {http://dl.acm.org/citation.cfm?doid=2736277.2741093},
	doi = {10.1145/2736277.2741093},
	language = {en},
	urldate = {2019-02-17},
	booktitle = {Proceedings of the 24th {International} {Conference} on {World} {Wide} {Web} - {WWW} '15},
	publisher = {ACM Press},
	author = {Tang, Jian and Qu, Meng and Wang, Mingzhe and Zhang, Ming and Yan, Jun and Mei, Qiaozhu},
	year = {2015},
	pages = {1067--1077}
}

@article{doria_contribution_2016,
	title = {Contribution of cholesterol and oxysterols to the pathophysiology of {Parkinson}'s disease},
	volume = {101},
	issn = {08915849},
	url = {https://linkinghub.elsevier.com/retrieve/pii/S0891584916304506},
	doi = {10.1016/j.freeradbiomed.2016.10.008},
	language = {en},
	urldate = {2019-02-22},
	journal = {Free Radical Biology and Medicine},
	author = {Doria, Margaux and Maugest, Lucie and Moreau, Thibault and Lizard, Gérard and Vejux, Anne},
	month = dec,
	year = {2016},
	pages = {393--400}
}

@article{hoyt_re-curation_2019,
	title = {Re-curation and {Rational} {Enrichment} of {Knowledge} {Graphs} in {Biological} {Expression} {Language}: {Supplementary} {Information}},
	shorttitle = {Re-curation and {Rational} {Enrichment} of {Knowledge} {Graphs} in {Biological} {Expression} {Language}},
	url = {http://biorxiv.org/lookup/doi/10.1101/536409},
	doi = {10.1101/536409},
	abstract = {The rapid accumulation of new biomedical literature not only causes curated knowledge graphs to become outdated and incomplete, but also makes manual curation an impractical and unsustainable solution. Automated or semi-automated workflows are necessary to assist in prioritizing and curating the literature to update and enrich knowledge graphs. We have developed two workflows: one for re-curating a given knowledge graph to assure its syntactic and semantic quality and another for rationally enriching it by manually revising automatically extracted relations for nodes with low information density. We applied these workflows to the knowledge graphs encoded in Biological Expression Language from the NeuroMMSig database using content that was pre-extracted from MEDLINE abstracts and PubMed Central full text articles using text mining output integrated by INDRA. We have made this workflow freely available at https://github.com/bel-enrichment.},
	urldate = {2019-02-24},
	journal = {bioRxiv},
	author = {Hoyt, Charles and Domingo-Fernandez, Daniel and Aldisi, Rana and Xu, Lingling and Kolpeja, Kristian and Spalek, Sandra and Wollert, Esther and Bachman, John and Gyori, Benjamin and Greene, Patrick and Hofmann-Apitius, Martin},
	month = jan,
	year = {2019}
}

@article{ciani_role_2014,
	title = {The role of health technology assessment bodies in shaping drug development},
	issn = {1177-8881},
	url = {http://www.dovepress.com/the-role-of-health-technology-assessment-bodies-in-shaping-drug-develo-peer-reviewed-article-DDDT},
	doi = {10.2147/DDDT.S49935},
	language = {en},
	urldate = {2019-03-07},
	journal = {Drug Design, Development and Therapy},
	author = {Ciani, Oriana and Jommi, Claudio},
	month = nov,
	year = {2014},
	pages = {2273}
}

@article{dimitri_drugclust:_2017,
	title = {{DrugClust}: {A} machine learning approach for drugs side effects prediction},
	volume = {68},
	issn = {14769271},
	shorttitle = {{DrugClust}},
	url = {https://linkinghub.elsevier.com/retrieve/pii/S1476927116302195},
	doi = {10.1016/j.compbiolchem.2017.03.008},
	language = {en},
	urldate = {2019-03-07},
	journal = {Computational Biology and Chemistry},
	author = {Dimitri, Giovanna Maria and Lió, Pietro},
	month = jun,
	year = {2017},
	pages = {204--210}
}

@article{boolell_sildenafil:_1996,
	title = {Sildenafil: an orally active type 5 cyclic {GMP}-specific phosphodiesterase inhibitor for the treatment of penile erectile dysfunction},
	volume = {8},
	issn = {0955-9930},
	shorttitle = {Sildenafil},
	abstract = {Sildenafil (Viagra, UK-92,480) is a novel oral agent under development for the treatment of penile erectile dysfunction. Erection is dependent on nitric oxide and its second messenger, cyclic guanosine monophosphate (cGMP). However, the relative importance of phosphodiesterase (PDE) isozymes is not clear. We have identified both cGMP- and cyclic adenosine monophosphate-specific phosphodiesterases (PDEs) in human corpora cavernosa in vitro. The main PDE activity in this tissue was due to PDE5, with PDE2 and 3 also identified. Sildenafil is a selective inhibitor of PDE5 with a mean IC50 of 0.0039 microM. In human volunteers, we have shown sildenafil to have suitable pharmacokinetic and pharmacodynamic properties (rapid absorption, relatively short half-life, no significant effect on heart rate and blood pressure) for an oral agent to be taken, as required, prior to sexual activity. Moreover, in a clinical study of 12 patients with erectile dysfunction without an established organic cause, we have shown sildenafil to enhance the erectile response (duration and rigidity of erection) to visual sexual stimulation, thus highlighting the important role of PDE5 in human penile erection. Sildenafil holds promise as a new effective oral treatment for penile erectile dysfunction.},
	language = {eng},
	number = {2},
	journal = {International Journal of Impotence Research},
	author = {Boolell, M. and Allen, M. J. and Ballard, S. A. and Gepi-Attee, S. and Muirhead, G. J. and Naylor, A. M. and Osterloh, I. H. and Gingell, C.},
	month = jun,
	year = {1996},
	pmid = {8858389},
	keywords = {Administration, Oral, Cross-Over Studies, Cyclic GMP, Double-Blind Method, Enzyme Inhibitors, Erectile Dysfunction, Humans, Isoenzymes, Male, Middle Aged, Penis, Phosphodiesterase Inhibitors, Phosphoric Diester Hydrolases, Piperazines, Purines, Sildenafil Citrate, Sulfones, Treatment Outcome},
	pages = {47--52}
}

@article{campillos_drug_2008,
	title = {Drug target identification using side-effect similarity},
	volume = {321},
	issn = {1095-9203},
	doi = {10.1126/science.1158140},
	abstract = {Targets for drugs have so far been predicted on the basis of molecular or cellular features, for example, by exploiting similarity in chemical structure or in activity across cell lines. We used phenotypic side-effect similarities to infer whether two drugs share a target. Applied to 746 marketed drugs, a network of 1018 side effect-driven drug-drug relations became apparent, 261 of which are formed by chemically dissimilar drugs from different therapeutic indications. We experimentally tested 20 of these unexpected drug-drug relations and validated 13 implied drug-target relations by in vitro binding assays, of which 11 reveal inhibition constants equal to less than 10 micromolar. Nine of these were tested and confirmed in cell assays, documenting the feasibility of using phenotypic information to infer molecular interactions and hinting at new uses of marketed drugs.},
	language = {eng},
	number = {5886},
	journal = {Science (New York, N.Y.)},
	author = {Campillos, Monica and Kuhn, Michael and Gavin, Anne-Claude and Jensen, Lars Juhl and Bork, Peer},
	month = jul,
	year = {2008},
	pmid = {18621671},
	keywords = {Humans, Adverse Drug Reaction Reporting Systems, Algorithms, Chemistry, Pharmaceutical, Databases, Factual, Drug Evaluation, Preclinical, Drug Labeling, Drug Therapy, Drug-Related Side Effects and Adverse Reactions, Pharmaceutical Preparations, Probability, Proteins},
	pages = {263--266}
}

@article{vargesson_thalidomideinduced_2015,
	title = {Thalidomide‐induced teratogenesis: {History} and mechanisms},
	volume = {105},
	issn = {1542-975X},
	shorttitle = {Thalidomide‐induced teratogenesis},
	url = {https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4737249/},
	doi = {10.1002/bdrc.21096},
	abstract = {Nearly 60 years ago thalidomide was prescribed to treat morning sickness in pregnant women. What followed was the biggest man‐made medical disaster ever, where over 10,000 children were born with a range of severe and debilitating malformations. Despite this, the drug is now used successfully to treat a range of adult conditions, including multiple myeloma and complications of leprosy. Tragically, a new generation of thalidomide damaged children has been identified in Brazil. Yet, how thalidomide caused its devastating effects in the forming embryo remains unclear. However, studies in the past few years have greatly enhanced our understanding of the molecular mechanisms the drug. This review will look at the history of the drug, and the range and type of damage the drug caused, and outline the mechanisms of action the drug uses including recent molecular advances and new findings. Some of the remaining challenges facing thalidomide biologists are also discussed. Birth Defects Research (Part C) 105:140–156, 2015. © 2015 The Authors Birth Defects Research Part C: Embryo Today: Reviews Published by Wiley Periodicals, Inc.},
	number = {2},
	urldate = {2019-03-07},
	journal = {Birth Defects Research},
	author = {Vargesson, Neil},
	month = jun,
	year = {2015},
	pmid = {26043938},
	pmcid = {PMC4737249},
	pages = {140--156}
}

@article{zhang_network_2017,
	title = {Network {Representation} {Learning}: {A} {Survey}},
	shorttitle = {Network {Representation} {Learning}},
	url = {http://arxiv.org/abs/1801.05852},
	abstract = {With the widespread use of information technologies, information networks are becoming increasingly popular to capture complex relationships across various disciplines, such as social networks, citation networks, telecommunication networks, and biological networks. Analyzing these networks sheds light on different aspects of social life such as the structure of societies, information diffusion, and communication patterns. In reality, however, the large scale of information networks often makes network analytic tasks computationally expensive or intractable. Network representation learning has been recently proposed as a new learning paradigm to embed network vertices into a low-dimensional vector space, by preserving network topology structure, vertex content, and other side information. This facilitates the original network to be easily handled in the new vector space for further analysis. In this survey, we perform a comprehensive review of the current literature on network representation learning in the data mining and machine learning field. We propose new taxonomies to categorize and summarize the state-of-the-art network representation learning techniques according to the underlying learning mechanisms, the network information intended to preserve, as well as the algorithmic designs and methodologies. We summarize evaluation protocols used for validating network representation learning including published benchmark datasets, evaluation methods, and open source algorithms. We also perform empirical studies to compare the performance of representative algorithms on common datasets, and analyze their computational complexity. Finally, we suggest promising research directions to facilitate future study.},
	urldate = {2019-03-07},
	journal = {arXiv:1801.05852 [cs, stat]},
	author = {Zhang, Daokun and Yin, Jie and Zhu, Xingquan and Zhang, Chengqi},
	month = dec,
	year = {2017},
	note = {arXiv: 1801.05852},
	keywords = {Computer Science - Social and Information Networks, Computer Science - Machine Learning, Statistics - Machine Learning}
}

@misc{noauthor_sider_nodate,
	title = {{SIDER} {Side} {Effect} {Resource}},
	url = {http://sideeffects.embl.de/},
	urldate = {2019-03-14},
}

@article{kuhn_side_2010,
	title = {A side effect resource to capture phenotypic effects of drugs},
	volume = {6},
	issn = {1744-4292},
	url = {http://msb.embopress.org/cgi/doi/10.1038/msb.2009.98},
	doi = {10.1038/msb.2009.98},
	urldate = {2019-03-14},
	journal = {Molecular Systems Biology},
	author = {Kuhn, Michael and Campillos, Monica and Letunic, Ivica and Jensen, Lars Juhl and Bork, Peer},
	month = jan,
	year = {2010}
}

@article{pourpak_understanding_2008,
	title = {Understanding adverse drug reactions and drug allergies: principles, diagnosis and treatment aspects},
	volume = {2},
	issn = {1872-213X},
	shorttitle = {Understanding adverse drug reactions and drug allergies},
	abstract = {Adverse Drug Reactions (ADRs) and drug allergies- as a subset of ADRs- make a significant public health concern, complicating 5 to 15\% of therapeutic drug courses. They may result in diminished quality of life, increased physician visits, health care costs, hospitalizations, and even death. The incidence of serious ADRs in hospitalized patients was estimated to be 6.7\% and for fatal ADRs to be 0.32\%, so recognizing and taking action on ADRs is an important aspect of medication management. Allergic reactions to drugs refer to those ADRs that involve immune mechanisms which account up to 15\% of ADRs and can be identified as being a type I through IV immune reaction that the most common immunologic mechanism is IgE-mediated- type I reaction. Clinical manifestations of allergic reactions range from pruritus and rash to serious reactions such as systemic anaphylaxis and cardiovascular emergencies and they are responsible for 2-3\% of hospitalized patients. Health professionals should be aware of the ADRs presenting clinical features and the risk factors and should be able to differentiate between allergic and non-allergic adverse drug reactions. This will lead to increased opportunities to review drug selection and prescribing practices affecting patients' outcome. This article will review the definition and estimated incidence, the features, classification and types of ADRs and drug allergies and related patents. It will highlight the role of detecting, reporting, and assessing suspected ADRs and drug allergies in the most clinically relevant drugs group. Priorities in the evaluation and management of the conditions of patients who have experienced allergic and non-allergic drug reactions also will be discussed.},
	language = {eng},
	number = {1},
	journal = {Recent Patents on Inflammation \& Allergy Drug Discovery},
	author = {Pourpak, Zahra and Fazlollahi, Mohammad R. and Fattahi, Fatemeh},
	month = jan,
	year = {2008},
	pmid = {19075990},
	keywords = {Humans, Treatment Outcome, Adverse Drug Reaction Reporting Systems, Drug-Related Side Effects and Adverse Reactions, Drug Hypersensitivity, Patents as Topic, Quality of Life, Risk Factors},
	pages = {24--46}
}

@article{mcgarry_resko:_2018,
	title = {{RESKO}: {Repositioning} drugs by using side effects and knowledge from ontologies},
	volume = {160},
	issn = {09507051},
	shorttitle = {{RESKO}},
	url = {https://linkinghub.elsevier.com/retrieve/pii/S0950705118303332},
	doi = {10.1016/j.knosys.2018.06.017},
	language = {en},
	urldate = {2019-03-15},
	journal = {Knowledge-Based Systems},
	author = {McGarry, Ken and Graham, Yitka and McDonald, Sharon and Rashid, Anuam},
	month = nov,
	year = {2018},
	pages = {34--48}
}

@article{kuhn_systematic_2014,
	title = {Systematic identification of proteins that elicit drug side effects},
	volume = {9},
	issn = {1744-4292},
	url = {http://msb.embopress.org/cgi/doi/10.1038/msb.2013.10},
	doi = {10.1038/msb.2013.10},
	language = {en},
	number = {1},
	urldate = {2019-03-15},
	journal = {Molecular Systems Biology},
	author = {Kuhn, M. and Al Banchaabouchi, M. and Campillos, M. and Jensen, L. J. and Gross, C. and Gavin, A.-C. and Bork, P.},
	month = apr,
	year = {2014},
	pages = {663--663}
}

@article{wu_computational_2013,
	title = {Computational drug repositioning through heterogeneous network clustering},
	volume = {7 Suppl 5},
	issn = {1752-0509},
	doi = {10.1186/1752-0509-7-S5-S6},
	abstract = {BACKGROUND: Given the costly and time consuming process and high attrition rates in drug discovery and development, drug repositioning or drug repurposing is considered as a viable strategy both to replenish the drying out drug pipelines and to surmount the innovation gap. Although there is a growing recognition that mechanistic relationships from molecular to systems level should be integrated into drug discovery paradigms, relatively few studies have integrated information about heterogeneous networks into computational drug-repositioning candidate discovery platforms.
RESULTS: Using known disease-gene and drug-target relationships from the KEGG database, we built a weighted disease and drug heterogeneous network. The nodes represent drugs or diseases while the edges represent shared gene, biological process, pathway, phenotype or a combination of these features. We clustered this weighted network to identify modules and then assembled all possible drug-disease pairs (putative drug repositioning candidates) from these modules. We validated our predictions by testing their robustness and evaluated them by their overlap with drug indications that were either reported in published literature or investigated in clinical trials.
CONCLUSIONS: Previous computational approaches for drug repositioning focused either on drug-drug and disease-disease similarity approaches whereas we have taken a more holistic approach by considering drug-disease relationships also. Further, we considered not only gene but also other features to build the disease drug networks. Despite the relative simplicity of our approach, based on the robustness analyses and the overlap of some of our predictions with drug indications that are under investigation, we believe our approach could complement the current computational approaches for drug repositioning candidate discovery.},
	language = {eng},
	journal = {BMC systems biology},
	author = {Wu, Chao and Gudivada, Ranga C. and Aronow, Bruce J. and Jegga, Anil G.},
	year = {2013},
	pmid = {24564976},
	pmcid = {PMC4029299},
	keywords = {Humans, Databases, Factual, Alzheimer Disease, Amyloid Precursor Protein Secretases, Anilides, Basal Cell Nevus Syndrome, Cluster Analysis, Computational Biology, Computer Graphics, Drug Repositioning, Hidradenitis Suppurativa, Phenotype, Protease Inhibitors, Pyridines},
	pages = {S6}
}

@article{luo_drug_2016,
	title = {Drug repositioning based on comprehensive similarity measures and {Bi}-{Random} walk algorithm},
	volume = {32},
	issn = {1367-4811},
	doi = {10.1093/bioinformatics/btw228},
	abstract = {MOTIVATION: Drug repositioning, which aims to identify new indications for existing drugs, offers a promising alternative to reduce the total time and cost of traditional drug development. Many computational strategies for drug repositioning have been proposed, which are based on similarities among drugs and diseases. Current studies typically use either only drug-related properties (e.g. chemical structures) or only disease-related properties (e.g. phenotypes) to calculate drug or disease similarity, respectively, while not taking into account the influence of known drug-disease association information on the similarity measures.
RESULTS: In this article, based on the assumption that similar drugs are normally associated with similar diseases and vice versa, we propose a novel computational method named MBiRW, which utilizes some comprehensive similarity measures and Bi-Random walk (BiRW) algorithm to identify potential novel indications for a given drug. By integrating drug or disease features information with known drug-disease associations, the comprehensive similarity measures are firstly developed to calculate similarity for drugs and diseases. Then drug similarity network and disease similarity network are constructed, and they are incorporated into a heterogeneous network with known drug-disease interactions. Based on the drug-disease heterogeneous network, BiRW algorithm is adopted to predict novel potential drug-disease associations. Computational experiment results from various datasets demonstrate that the proposed approach has reliable prediction performance and outperforms several recent computational drug repositioning approaches. Moreover, case studies of five selected drugs further confirm the superior performance of our method to discover potential indications for drugs practically.
AVAILABILITY AND IMPLEMENTATION: http://github.com//bioinfomaticsCSU/MBiRW CONTACT: jxwang@mail.csu.edu.cn
SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.},
	language = {eng},
	number = {17},
	journal = {Bioinformatics (Oxford, England)},
	author = {Luo, Huimin and Wang, Jianxin and Li, Min and Luo, Junwei and Peng, Xiaoqing and Wu, Fang-Xiang and Pan, Yi},
	year = {2016},
	pmid = {27153662},
	keywords = {Algorithms, Computational Biology, Drug Repositioning, Models, Theoretical},
	pages = {2664--2671},
}

@article{wishart_drugbank_2018,
	title = {{DrugBank} 5.0: a major update to the {DrugBank} database for 2018},
	volume = {46},
	issn = {0305-1048, 1362-4962},
	shorttitle = {{DrugBank} 5.0},
	url = {http://academic.oup.com/nar/article/46/D1/D1074/4602867},
	doi = {10.1093/nar/gkx1037},
	language = {en},
	number = {D1},
	urldate = {2019-05-07},
	journal = {Nucleic Acids Research},
	author = {Wishart, David S and Feunang, Yannick D and Guo, An C and Lo, Elvis J and Marcu, Ana and Grant, Jason R and Sajed, Tanvir and Johnson, Daniel and Li, Carin and Sayeeda, Zinat and Assempour, Nazanin and Iynkkaran, Ithayavani and Liu, Yifeng and Maciejewski, Adam and Gale, Nicola and Wilson, Alex and Chin, Lucy and Cummings, Ryan and Le, Diana and Pon, Allison and Knox, Craig and Wilson, Michael},
	month = jan,
	year = {2018},
	pages = {D1074--D1082},
}

@article{montanari_virtual_2017,
	title = {Virtual {Screening} of {DrugBank} {Reveals} {Two} {Drugs} as {New} {BCRP} {Inhibitors}},
	volume = {22},
	issn = {2472-5552, 2472-5560},
	url = {http://journals.sagepub.com/doi/10.1177/1087057116657513},
	doi = {10.1177/1087057116657513},
	language = {en},
	number = {1},
	urldate = {2019-05-07},
	journal = {SLAS DISCOVERY: Advancing Life Sciences R\&D},
	author = {Montanari, Floriane and Cseke, Anna and Wlcek, Katrin and Ecker, Gerhard F.},
	month = jan,
	year = {2017},
	pages = {86--93}
}

@article{wang_exploring_2014,
	title = {Exploring the associations between drug side-effects and therapeutic indications},
	volume = {51},
	issn = {1532-0464},
	url = {http://www.sciencedirect.com/science/article/pii/S1532046414000811},
	doi = {https://doi.org/10.1016/j.jbi.2014.03.014},
	abstract = {Drug therapeutic indications and side-effects are both measurable patient phenotype changes in response to the treatment. Inferring potential drug therapeutic indications and identifying clinically interesting drug side-effects are both important and challenging tasks. Previous studies have utilized either chemical structures or protein targets to predict indications and side-effects. In this study, we compared drug therapeutic indication prediction using various information including chemical structures, protein targets and side-effects. We also compared drug side-effect prediction with various information sources including chemical structures, protein targets and therapeutic indication. Prediction performance based on 10-fold cross-validation demonstrates that drug side-effects and therapeutic indications are the most predictive information source for each other. In addition, we extracted 6706 statistically significant indication-side-effect associations from all known drug-disease and drug-side-effect relationships. We further developed a novel user interface that allows the user to interactively explore these associations in the form of a dynamic bipartitie graph. Many relationship pairs provide explicit repositioning hypotheses (e.g., drugs causing postural hypotension are potential candidates for hypertension) and clear adverse-reaction watch lists (e.g., drugs for heart failure possibly cause impotence). All data sets and highly correlated disease-side-effect relationships are available at http://astro.temple.edu/∼tua87106/druganalysis.html.},
	journal = {Journal of Biomedical Informatics},
	author = {Wang, Fei and Zhang, Ping and Cao, Nan and Hu, Jianying and Sorrentino, Robert},
	year = {2014},
	keywords = {Associations, Side-effects, Theraputic indications},
	pages = {15 -- 23}
}

@article{ashburn_drug_2004,
	title = {Drug repositioning: identifying and developing new uses for existing drugs},
	volume = {3},
	issn = {1474-1776, 1474-1784},
	shorttitle = {Drug repositioning},
	url = {http://www.nature.com/articles/nrd1468},
	doi = {10.1038/nrd1468},
	language = {en},
	number = {8},
	urldate = {2019-06-24},
	journal = {Nature Reviews Drug Discovery},
	author = {Ashburn, Ted T. and Thor, Karl B.},
	month = aug,
	year = {2004},
	pages = {673--683}
}

@incollection{parthasarathi_chapter_2018,
	title = {Chapter 5 - {In} {Silico} {Approaches} for {Predictive} {Toxicology}},
	isbn = {978-0-12-804667-8},
	url = {http://www.sciencedirect.com/science/article/pii/B9780128046678000055},
	abstract = {In silico toxicology plays a vital role in the assessment of safety/toxicity of chemicals and the drug development process. Computational approaches continue to increase in capability and applicability to predictive toxicology. These advanced methodology are utilized in various stages of the development of substance by prediction of properties that correlate with toxicity endpoints, structure activity relationship models for new chemical formulations, and building/retrieving information on chemical databases. This chapter covers different aspects of computational approaches that focuses on in silico toxicology, that aims to complement prevailing in vitro/in vivo toxicity tests to predict toxicity and prioritize chemicals/drugs to minimize harmful effects. The state-of-the-art computational approaches used in in silico toxicology are highlighted in this chapter. Special attention has been drawn on the usefulness of quantitative structure activity relationship models for toxicity prediction, descriptor development for predictive toxicology and databases/in silico tools used for toxicity prediction.},
	urldate = {2019-07-04},
	booktitle = {In {Vitro} {Toxicology}},
	publisher = {Academic Press},
	author = {Parthasarathi, Ramakrishnan and Dhawan, Alok},
	editor = {Dhawan, Alok and Kwon, Seok},
	month = jan,
	year = {2018},
	doi = {10.1016/B978-0-12-804667-8.00005-5},
	keywords = {Descriptors, In silico toxicology, Predictive toxicology, QSAR, Quantitative structure toxicity relationship, Toxicity database, Toxicity endpoints},
	pages = {91--109}
}

@inproceedings{ehrlinger_towards_2016,
	title = {Towards a {Definition} of {Knowledge} {Graphs}},
	abstract = {Recently, the term knowledge graph has been used frequently in research and business, usually in close association with Semantic Web technologies, linked data, large-scale data analytics and cloud computing. Its popularity is clearly influenced by the introduction of Google’s Knowledge Graph in 2012, and since then the term has been widely used without a definition. A large variety of interpretations has hampered the evolution of a common understanding of knowledge graphs. Numerous research papers refer to Google’s Knowledge Graph, although no official documentation about the used methods exists. The prerequisite for widespread academic and commercial adoption of a concept or technology is a common understanding, based ideally on a definition that is free from ambiguity. We tackle this issue by discussing and defining the term knowledge graph, considering its history and diversity in interpretations and use. Our goal is to propose a definition of knowledge graphs that serves as basis for discussions on this topic and contributes to a common vision.},
	booktitle = {{SEMANTiCS}},
	author = {Ehrlinger, Lisa and Wöß, Wolfram},
	year = {2016},
	keywords = {Cloud computing, Documentation, Knowledge Graph, Linked data, Semantic Web}
}

@book{foster_basic_2015,
	title = {Basic {Pharmacology}},
	isbn = {978-1-4831-4202-9},
	abstract = {Basic Pharmacology, Third Edition aims to present accounts of drug actions and their mechanisms in a compact, inexpensive, and updated form, and explain the basis of the therapeutic exploitation of drugs. This book is divided into sections that follow a particular theme and is introduced by the relevant pharmacological general principles. In each section, the major groups of drugs related to the theme are discussed with detailed expositions of the important “type substances. Drugs of lesser importance are placed in proper context. A list of abbreviations that are referenced throughout the book is provided after the introduction. An index is also included at the end. This edition is designed to help students taking pharmacology, including medical students of subjects affiliated to medicine, to appreciate the rationale underlying the uses of drugs in therapeutics.},
	language = {en},
	publisher = {Elsevier},
	author = {Foster, R. W.},
	month = may,
	year = {2015},
	note = {Google-Books-ID: WbMgBQAAQBAJ},
	keywords = {Medical / Pharmacology}
}

@book{satoskar_pharmacology_1973,
	title = {Pharmacology and {Pharmacotherapeutics}},
	isbn = {978-81-7991-527-1},
	language = {en},
	publisher = {Popular Prakashan},
	author = {Satoskar, R. S. and Rege, S. D. Bhandarkar \&nirmala N.},
	year = {1973},
	note = {Google-Books-ID: 7d493VOD4P8C}
}

@book{rang_rang_2014,
	title = {Rang \& {Dale}'s {Pharmacology} {E}-{Book}: with {STUDENT} {CONSULT} {Online} {Access}},
	isbn = {978-0-7020-5497-6},
	shorttitle = {Rang \& {Dale}'s {Pharmacology} {E}-{Book}},
	abstract = {For 25 years, Rang and Dale’s Pharmacology has delivered the core basic and clinical science information required by students and healthcare practitioners worldwide. Authors H. P. Rang, J. M. Ritter, R. J. Flower, and G. Henderson have ensured that the 8th Edition of this easy-to-read, comprehensive text continues the tradition of excellence with new coverage of drugs affecting the skin and new components online at studentconsult.com.Consult this title on your favorite e-reader. Get the essential pharmacology information you need from one authoritative source with an outstanding global reputation for excellence.  Progress confidently through all relevant aspects of pharmacology, beginning with a molecular understanding of receptors and drug actions through clinical uses of key groups of drugs.  Find important content quickly thanks to a color-coded layout that enables easy navigation and cross-referencing.  Master difficult concepts with Key Points boxes, Clinical Uses boxes, and full-color illustrations throughout. Stay up to date with new information in the field, including an all-new chapter on drugs that affect the skin. Take advantage of new and unique features online, including 500+ chapter-specific multiple choice questions for immediate self-assessment.  eBook version included! For the first time, you can access the entire book online or offline across all devices with the Student Consult eBook!},
	language = {en},
	publisher = {Elsevier Health Sciences},
	author = {Rang, Humphrey P. and Ritter, James M. and Flower, Rod J. and Henderson, Graeme},
	month = dec,
	year = {2014},
	note = {Google-Books-ID: iOLTBQAAQBAJ},
	keywords = {Medical / Pharmacology}
}

@article{gashaw_what_2011,
	title = {What makes a good drug target?},
	volume = {16},
	issn = {13596446},
	url = {https://linkinghub.elsevier.com/retrieve/pii/S1359644611002972},
	doi = {10.1016/j.drudis.2011.09.007},
	language = {en},
	number = {23-24},
	urldate = {2019-07-17},
	journal = {Drug Discovery Today},
	author = {Gashaw, Isabella and Ellinghaus, Peter and Sommer, Anette and Asadullah, Khusru},
	month = dec,
	year = {2011},
	pages = {1037--1043}
}

@article{kuhn_sider_2016,
	title = {The {SIDER} database of drugs and side effects},
	volume = {44},
	issn = {0305-1048},
	url = {https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4702794/},
	doi = {10.1093/nar/gkv1075},
	abstract = {Unwanted side effects of drugs are a burden on patients and a severe impediment in the development of new drugs. At the same time, adverse drug reactions (ADRs) recorded during clinical trials are an important source of human phenotypic data. It is therefore essential to combine data on drugs, targets and side effects into a more complete picture of the therapeutic mechanism of actions of drugs and the ways in which they cause adverse reactions. To this end, we have created the SIDER (‘Side Effect Resource’, http://sideeffects.embl.de) database of drugs and ADRs. The current release, SIDER 4, contains data on 1430 drugs, 5880 ADRs and 140 064 drug–ADR pairs, which is an increase of 40\% compared to the previous version. For more fine-grained analyses, we extracted the frequency with which side effects occur from the package inserts. This information is available for 39\% of drug–ADR pairs, 19\% of which can be compared to the frequency under placebo treatment. SIDER furthermore contains a data set of drug indications, extracted from the package inserts using Natural Language Processing. These drug indications are used to reduce the rate of false positives by identifying medical terms that do not correspond to ADRs.},
	number = {Database issue},
	urldate = {2019-07-21},
	journal = {Nucleic Acids Research},
	author = {Kuhn, Michael and Letunic, Ivica and Jensen, Lars Juhl and Bork, Peer},
	month = jan,
	year = {2016},
	pmid = {26481350},
	pmcid = {PMC4702794},
	pages = {D1075--D1079}
}

@article{goyal_graph_2018,
	title = {Graph {Embedding} {Techniques}, {Applications}, and {Performance}: {A} {Survey}},
	volume = {151},
	issn = {09507051},
	shorttitle = {Graph {Embedding} {Techniques}, {Applications}, and {Performance}},
	url = {http://arxiv.org/abs/1705.02801},
	doi = {10.1016/j.knosys.2018.03.022},
	abstract = {Graphs, such as social networks, word co-occurrence networks, and communication networks, occur naturally in various real-world applications. Analyzing them yields insight into the structure of society, language, and different patterns of communication. Many approaches have been proposed to perform the analysis. Recently, methods which use the representation of graph nodes in vector space have gained traction from the research community. In this survey, we provide a comprehensive and structured analysis of various graph embedding techniques proposed in the literature. We first introduce the embedding task and its challenges such as scalability, choice of dimensionality, and features to be preserved, and their possible solutions. We then present three categories of approaches based on factorization methods, random walks, and deep learning, with examples of representative algorithms in each category and analysis of their performance on various tasks. We evaluate these state-of-the-art methods on a few common datasets and compare their performance against one another. Our analysis concludes by suggesting some potential applications and future directions. We finally present the open-source Python library we developed, named GEM (Graph Embedding Methods, available at https://github.com/palash1992/GEM), which provides all presented algorithms within a unified interface to foster and facilitate research on the topic.},
	urldate = {2019-07-21},
	journal = {Knowledge-Based Systems},
	author = {Goyal, Palash and Ferrara, Emilio},
	month = jul,
	year = {2018},
	note = {arXiv: 1705.02801},
	keywords = {Computer Science - Social and Information Networks, Computer Science - Machine Learning, Physics - Data Analysis, Statistics and Probability},
	pages = {78--94}
}

@article{wang_knowledge_2017,
	title = {Knowledge {Graph} {Embedding}: {A} {Survey} of {Approaches} and {Applications}},
	volume = {29},
	issn = {1041-4347},
	shorttitle = {Knowledge {Graph} {Embedding}},
	url = {http://ieeexplore.ieee.org/document/8047276/},
	doi = {10.1109/TKDE.2017.2754499},
	number = {12},
	urldate = {2019-07-22},
	journal = {IEEE Transactions on Knowledge and Data Engineering},
	author = {Wang, Quan and Mao, Zhendong and Wang, Bin and Guo, Li},
	month = dec,
	year = {2017},
	pages = {2724--2743}
}

@inproceedings{bordes_translating_2013,
	address = {USA},
	series = {{NIPS}'13},
	title = {Translating {Embeddings} for {Modeling} {Multi}-relational {Data}},
	url = {http://dl.acm.org/citation.cfm?id=2999792.2999923},
	abstract = {We consider the problem of embedding entities and relationships of multi-relational data in low-dimensional vector spaces. Our objective is to propose a canonical model which is easy to train, contains a reduced number of parameters and can scale up to very large databases. Hence, we propose TransE, a method which models relationships by interpreting them as translations operating on the low-dimensional embeddings of the entities. Despite its simplicity, this assumption proves to be powerful since extensive experiments show that TransE significantly outperforms state-of-the-art methods in link prediction on two knowledge bases. Besides, it can be successfully trained on a large scale data set with 1M entities, 25k relationships and more than 17M training samples.},
	urldate = {2019-07-22},
	booktitle = {Proceedings of the 26th {International} {Conference} on {Neural} {Information} {Processing} {Systems} - {Volume} 2},
	publisher = {Curran Associates Inc.},
	author = {Bordes, Antoine and Usunier, Nicolas and Garcia-Durán, Alberto and Weston, Jason and Yakhnenko, Oksana},
	year = {2013},
	note = {event-place: Lake Tahoe, Nevada},
	pages = {2787--2795}
}

@inproceedings{wang_knowledge_2014,
	title = {Knowledge {Graph} {Embedding} by {Translating} on {Hyperplanes}},
	abstract = {We deal with embedding a large scale knowledge graph composed of entities and relations into a continuous vector space. TransE is a promising method proposed recently, which is very efficient while achieving state-of-the-art predictive performance. We discuss some mapping properties of relations which should be considered in embedding, such as reflexive, one-to-many, many-to-one, and many-to-many. We note that TransE does not do well in dealing with these properties. Some complex models are capable of preserving these mapping properties but sacrifice efficiency in the process. To make a good trade-off between model capacity and efficiency, in this paper we propose TransH which models a relation as a hyperplane together with a translation operation on it. In this way, we can well preserve the above mapping properties of relations with almost the same model complexity of TransE. Additionally, as a practical knowledge graph is often far from completed, how to construct negative examples to reduce false negative labels in training is very important. Utilizing the one-to-many/many-to-one mapping property of a relation, we propose a simple trick to reduce the possibility of false negative labeling. We conduct extensive experiments on link prediction, triplet classification and fact extraction on benchmark datasets like WordNet and Freebase. Experiments show TransH delivers significant improvements over TransE on predictive accuracy with comparable capability to scale up.},
	booktitle = {{AAAI}},
	author = {Wang, Zhen and Zhang, Jianwen and Feng, Jianlin and Chen, Zhigang},
	year = {2014},
	keywords = {Knowledge Graph, Benchmark (computing), Entity, Experiment, Freebase, Graph embedding, Many-to-many (data model), Negative feedback, One-to-many (data model), Triplet state, WordNet}
}

@article{cai_comprehensive_2017,
	title = {A {Comprehensive} {Survey} of {Graph} {Embedding}: {Problems}, {Techniques} and {Applications}},
	shorttitle = {A {Comprehensive} {Survey} of {Graph} {Embedding}},
	url = {http://arxiv.org/abs/1709.07604},
	abstract = {Graph is an important data representation which appears in a wide diversity of real-world scenarios. Effective graph analytics provides users a deeper understanding of what is behind the data, and thus can benefit a lot of useful applications such as node classification, node recommendation, link prediction, etc. However, most graph analytics methods suffer the high computation and space cost. Graph embedding is an effective yet efficient way to solve the graph analytics problem. It converts the graph data into a low dimensional space in which the graph structural information and graph properties are maximally preserved. In this survey, we conduct a comprehensive review of the literature in graph embedding. We first introduce the formal definition of graph embedding as well as the related concepts. After that, we propose two taxonomies of graph embedding which correspond to what challenges exist in different graph embedding problem settings and how the existing work address these challenges in their solutions. Finally, we summarize the applications that graph embedding enables and suggest four promising future research directions in terms of computation efficiency, problem settings, techniques and application scenarios.},
	urldate = {2019-07-22},
	journal = {arXiv:1709.07604 [cs]},
	author = {Cai, Hongyun and Zheng, Vincent W. and Chang, Kevin Chen-Chuan},
	month = sep,
	year = {2017},
	note = {arXiv: 1709.07604},
	keywords = {Computer Science - Artificial Intelligence}
}

@article{lotfi_shahreza_review_2018,
	title = {A review of network-based approaches to drug repositioning},
	volume = {19},
	issn = {1477-4054},
	doi = {10.1093/bib/bbx017},
	abstract = {Experimental drug development is time-consuming, expensive and limited to a relatively small number of targets. However, recent studies show that repositioning of existing drugs can function more efficiently than de novo experimental drug development to minimize costs and risks. Previous studies have proven that network analysis is a versatile platform for this purpose, as the biological networks are used to model interactions between many different biological concepts. The present study is an attempt to review network-based methods in predicting drug targets for drug repositioning. For each method, the preferred type of data set is described, and their advantages and limitations are discussed. For each method, we seek to provide a brief description, as well as an evaluation based on its performance metrics.We conclude that integrating distinct and complementary data should be used because each type of data set reveals a unique aspect of information about an organism. We also suggest that applying a standard set of evaluation metrics and data sets would be essential in this fast-growing research domain.},
	language = {eng},
	number = {5},
	journal = {Briefings in Bioinformatics},
	author = {Lotfi Shahreza, Maryam and Ghadiri, Nasser and Mousavi, Sayed Rasoul and Varshosaz, Jaleh and Green, James R.},
	year = {2018},
	pmid = {28334136},
	keywords = {Humans, Drug-Related Side Effects and Adverse Reactions, Computational Biology, Drug Repositioning, Databases, Pharmaceutical, Drug Interactions, Gene Regulatory Networks, Machine Learning, Metabolic Networks and Pathways, Molecular Docking Simulation, Protein Interaction Maps},
	pages = {878--892}
}

@article{mizutani_relating_2012,
	title = {Relating drug-protein interaction network with drug side effects},
	volume = {28},
	issn = {1367-4803, 1460-2059},
	url = {https://academic.oup.com/bioinformatics/article-lookup/doi/10.1093/bioinformatics/bts383},
	doi = {10.1093/bioinformatics/bts383},
	language = {en},
	number = {18},
	urldate = {2019-07-23},
	journal = {Bioinformatics},
	author = {Mizutani, S. and Pauwels, E. and Stoven, V. and Goto, S. and Yamanishi, Y.},
	month = sep,
	year = {2012},
	pages = {i522--i528}
}

@article{scheiber_mapping_2009,
	title = {Mapping {Adverse} {Drug} {Reactions} in {Chemical} {Space}},
	volume = {52},
	issn = {0022-2623, 1520-4804},
	url = {https://pubs.acs.org/doi/10.1021/jm801546k},
	doi = {10.1021/jm801546k},
	language = {en},
	number = {9},
	urldate = {2019-07-23},
	journal = {Journal of Medicinal Chemistry},
	author = {Scheiber, Josef and Jenkins, Jeremy L. and Sukuru, Sai Chetan K. and Bender, Andreas and Mikhailov, Dmitri and Milik, Mariusz and Azzaoui, Kamal and Whitebread, Steven and Hamon, Jacques and Urban, Laszlo and Glick, Meir and Davies, John W.},
	month = may,
	year = {2009},
	pages = {3103--3107}
}

@article{atias_algorithmic_2011,
	title = {An {Algorithmic} {Framework} for {Predicting} {Side} {Effects} of {Drugs}},
	volume = {18},
	issn = {1066-5277, 1557-8666},
	url = {http://www.liebertpub.com/doi/10.1089/cmb.2010.0255},
	doi = {10.1089/cmb.2010.0255},
	language = {en},
	number = {3},
	urldate = {2019-07-24},
	journal = {Journal of Computational Biology},
	author = {Atias, Nir and Sharan, Roded},
	month = mar,
	year = {2011},
	pages = {207--218}
}

@article{pauwels_predicting_2011,
	title = {Predicting drug side-effect profiles: a chemical fragment-based approach},
	volume = {12},
	issn = {1471-2105},
	shorttitle = {Predicting drug side-effect profiles},
	url = {http://bmcbioinformatics.biomedcentral.com/articles/10.1186/1471-2105-12-169},
	doi = {10.1186/1471-2105-12-169},
	language = {en},
	number = {1},
	urldate = {2019-07-24},
	journal = {BMC Bioinformatics},
	author = {Pauwels, Edouard and Stoven, Véronique and Yamanishi, Yoshihiro},
	year = {2011},
	pages = {169}
}

@article{yang_systematic_2011,
	title = {Systematic {Drug} {Repositioning} {Based} on {Clinical} {Side}-{Effects}},
	volume = {6},
	issn = {1932-6203},
	url = {https://dx.plos.org/10.1371/journal.pone.0028025},
	doi = {10.1371/journal.pone.0028025},
	language = {en},
	number = {12},
	urldate = {2019-07-24},
	journal = {PLoS ONE},
	author = {Yang, Lun and Agarwal, Pankaj},
	editor = {Csermely, Peter},
	month = dec,
	year = {2011},
	pages = {e28025}
}

@article{ye_construction_2014,
	title = {Construction of {Drug} {Network} {Based} on {Side} {Effects} and {Its} {Application} for {Drug} {Repositioning}},
	volume = {9},
	issn = {1932-6203},
	url = {http://dx.plos.org/10.1371/journal.pone.0087864},
	doi = {10.1371/journal.pone.0087864},
	language = {en},
	number = {2},
	urldate = {2019-07-24},
	journal = {PLoS ONE},
	author = {Ye, Hao and Liu, Qi and Wei, Jia},
	editor = {Keskin, Ozlem},
	month = feb,
	year = {2014},
	pages = {e87864},
}

@article{kim_pubchem_2019,
	title = {{PubChem} 2019 update: improved access to chemical data},
	volume = {47},
	issn = {1362-4962},
	shorttitle = {{PubChem} 2019 update},
	doi = {10.1093/nar/gky1033},
	abstract = {PubChem (https://pubchem.ncbi.nlm.nih.gov) is a key chemical information resource for the biomedical research community. Substantial improvements were made in the past few years. New data content was added, including spectral information, scientific articles mentioning chemicals, and information for food and agricultural chemicals. PubChem released new web interfaces, such as PubChem Target View page, Sources page, Bioactivity dyad pages and Patent View page. PubChem also released a major update to PubChem Widgets and introduced a new programmatic access interface, called PUG-View. This paper describes these new developments in PubChem.},
	language = {eng},
	number = {D1},
	journal = {Nucleic Acids Research},
	author = {Kim, Sunghwan and Chen, Jie and Cheng, Tiejun and Gindulyte, Asta and He, Jia and He, Siqian and Li, Qingliang and Shoemaker, Benjamin A. and Thiessen, Paul A. and Yu, Bo and Zaslavsky, Leonid and Zhang, Jian and Bolton, Evan E.},
	month = jan,
	year = {2019},
	pmid = {30371825},
	pmcid = {PMC6324075},
	pages = {D1102--D1109},
}

@article{kim_pubchem_2016,
	title = {{PubChem} {Substance} and {Compound} databases},
	volume = {44},
	issn = {1362-4962},
	doi = {10.1093/nar/gkv951},
	abstract = {PubChem (https://pubchem.ncbi.nlm.nih.gov) is a public repository for information on chemical substances and their biological activities, launched in 2004 as a component of the Molecular Libraries Roadmap Initiatives of the US National Institutes of Health (NIH). For the past 11 years, PubChem has grown to a sizable system, serving as a chemical information resource for the scientific research community. PubChem consists of three inter-linked databases, Substance, Compound and BioAssay. The Substance database contains chemical information deposited by individual data contributors to PubChem, and the Compound database stores unique chemical structures extracted from the Substance database. Biological activity data of chemical substances tested in assay experiments are contained in the BioAssay database. This paper provides an overview of the PubChem Substance and Compound databases, including data sources and contents, data organization, data submission using PubChem Upload, chemical structure standardization, web-based interfaces for textual and non-textual searches, and programmatic access. It also gives a brief description of PubChem3D, a resource derived from theoretical three-dimensional structures of compounds in PubChem, as well as PubChemRDF, Resource Description Framework (RDF)-formatted PubChem data for data sharing, analysis and integration with information contained in other databases.},
	language = {eng},
	number = {D1},
	journal = {Nucleic Acids Research},
	author = {Kim, Sunghwan and Thiessen, Paul A. and Bolton, Evan E. and Chen, Jie and Fu, Gang and Gindulyte, Asta and Han, Lianyi and He, Jane and He, Siqian and Shoemaker, Benjamin A. and Wang, Jiyao and Yu, Bo and Zhang, Jian and Bryant, Stephen H.},
	month = jan,
	year = {2016},
	pmid = {26400175},
	pmcid = {PMC4702940},
	keywords = {Pharmaceutical Preparations, Databases, Chemical, Internet, Molecular Structure, Software},
	pages = {D1202--1213},
}

@inproceedings{lin_learning_2015,
	series = {{AAAI}'15},
	title = {Learning {Entity} and {Relation} {Embeddings} for {Knowledge} {Graph} {Completion}},
	isbn = {978-0-262-51129-2},
	url = {http://dl.acm.org/citation.cfm?id=2886521.2886624},
	abstract = {Knowledge graph completion aims to perform link prediction between entities. In this paper, we consider the approach of knowledge graph embeddings. Recently, models such as TransE and TransH build entity and relation embeddings by regarding a relation as translation from head entity to tail entity. We note that these models simply put both entities and relations within the same semantic space. In fact, an entity may have multiple aspects and various relations may focus on different aspects of entities, which makes a common space insufficient for modeling. In this paper, we propose TransR to build entity and relation embeddings in separate entity space and relation spaces. Afterwards, we learn embeddings by first projecting entities from entity space to corresponding relation space and then building translations between projected entities. In experiments, we evaluate our models on three tasks including link prediction, triple classification and relational fact extraction. Experimental results show significant and consistent improvements compared to state-of-the-art baselines including TransE and TransH. The source code of this paper can be obtained from https://github.com/mrlyk423/relation\_extraction.},
	urldate = {2019-07-24},
	booktitle = {Proceedings of the {Twenty}-{Ninth} {AAAI} {Conference} on {Artificial} {Intelligence}},
	publisher = {AAAI Press},
	author = {Lin, Yankai and Liu, Zhiyuan and Sun, Maosong and Liu, Yang and Zhu, Xuan},
	year = {2015},
	note = {event-place: Austin, Texas},
	pages = {2181--2187}
}

@inproceedings{ou_asymmetric_2016,
	address = {New York, NY, USA},
	series = {{KDD} '16},
	title = {Asymmetric {Transitivity} {Preserving} {Graph} {Embedding}},
	isbn = {978-1-4503-4232-2},
	url = {http://doi.acm.org/10.1145/2939672.2939751},
	doi = {10.1145/2939672.2939751},
	abstract = {Graph embedding algorithms embed a graph into a vector space where the structure and the inherent properties of the graph are preserved. The existing graph embedding methods cannot preserve the asymmetric transitivity well, which is a critical property of directed graphs. Asymmetric transitivity depicts the correlation among directed edges, that is, if there is a directed path from u to v, then there is likely a directed edge from u to v. Asymmetric transitivity can help in capturing structures of graphs and recovering from partially observed graphs. To tackle this challenge, we propose the idea of preserving asymmetric transitivity by approximating high-order proximity which are based on asymmetric transitivity. In particular, we develop a novel graph embedding algorithm, High-Order Proximity preserved Embedding (HOPE for short), which is scalable to preserve high-order proximities of large scale graphs and capable of capturing the asymmetric transitivity. More specifically, we first derive a general formulation that cover multiple popular high-order proximity measurements, then propose a scalable embedding algorithm to approximate the high-order proximity measurements based on their general formulation. Moreover, we provide a theoretical upper bound on the RMSE (Root Mean Squared Error) of the approximation. Our empirical experiments on a synthetic dataset and three real-world datasets demonstrate that HOPE can approximate the high-order proximities significantly better than the state-of-art algorithms and outperform the state-of-art algorithms in tasks of reconstruction, link prediction and vertex recommendation.},
	urldate = {2019-07-24},
	booktitle = {Proceedings of the 22Nd {ACM} {SIGKDD} {International} {Conference} on {Knowledge} {Discovery} and {Data} {Mining}},
	publisher = {ACM},
	author = {Ou, Mingdong and Cui, Peng and Pei, Jian and Zhang, Ziwei and Zhu, Wenwu},
	year = {2016},
	note = {event-place: San Francisco, California, USA},
	keywords = {asymmetric transitivity, directed graph, graph embedding, high-order proximity},
	pages = {1105--1114}
}

@inproceedings{wang_structural_2016,
	address = {San Francisco, California, USA},
	title = {Structural {Deep} {Network} {Embedding}},
	isbn = {978-1-4503-4232-2},
	url = {http://dl.acm.org/citation.cfm?doid=2939672.2939753},
	doi = {10.1145/2939672.2939753},
	abstract = {Network embedding is an important method to learn low-dimensional representations of vertexes in networks, aiming to capture and preserve the network structure. Almost all the existing network embedding methods adopt shallow models. However, since the underlying network structure is complex, shallow models cannot capture the highly non-linear network structure, resulting in sub-optimal network representations. Therefore, how to find a method that is able to effectively capture the highly non-linear network structure and preserve the global and local structure is an open yet important problem. To solve this problem, in this paper we propose a Structural Deep Network Embedding method, namely SDNE. More specifically, we first propose a semi-supervised deep model, which has multiple layers of non-linear functions, thereby being able to capture the highly non-linear network structure. Then we propose to exploit the first-order and second-order proximity jointly to preserve the network structure. The second-order proximity is used by the unsupervised component to capture the global network structure. While the first-order proximity is used as the supervised information in the supervised component to preserve the local network structure. By jointly optimizing them in the semi-supervised deep model, our method can preserve both the local and global network structure and is robust to sparse networks. Empirically, we conduct the experiments on five real-world networks, including a language network, a citation network and three social networks. The results show that compared to the baselines, our method can reconstruct the original network significantly better and achieves substantial gains in three applications, i.e. multi-label classification, link prediction and visualization.},
	language = {en},
	urldate = {2019-07-24},
	booktitle = {Proceedings of the 22nd {ACM} {SIGKDD} {International} {Conference} on {Knowledge} {Discovery} and {Data} {Mining} - {KDD} '16},
	publisher = {ACM Press},
	author = {Wang, Daixin and Cui, Peng and Zhu, Wenwu},
	year = {2016},
	pages = {1225--1234}
}

@article{hoyt_integration_2019,
	title = {Integration of {Structured} {Biological} {Data} {Sources} using {Biological} {Expression} {Language}},
	copyright = {© 2019, Posted by Cold Spring Harbor Laboratory. This pre-print is available under a Creative Commons License (Attribution 4.0 International), CC BY 4.0, as described at http://creativecommons.org/licenses/by/4.0/},
	url = {https://www.biorxiv.org/content/10.1101/631812v1},
	doi = {10.1101/631812},
	abstract = {{\textless}h3{\textgreater}Abstract{\textless}/h3{\textgreater} {\textless}h3{\textgreater}Background{\textless}/h3{\textgreater} {\textless}p{\textgreater}The integration of heterogeneous, multiscale, and multimodal knowledge and data has become a common prerequisite for joint analysis to unravel the mechanisms and aetiologies of complex diseases. Because of its unique ability to capture this variety, Biological Expression Language (BEL) is well suited to be further used as a platform for semantic integration and harmonization in networks and systems biology.{\textless}/p{\textgreater}{\textless}h3{\textgreater}Results{\textless}/h3{\textgreater} {\textless}p{\textgreater}We have developed numerous independent packages capable of downloading, structuring, and serializing various biological data sources to BEL. Each Bio2BEL package is implemented in the Python programming language and distributed through GitHub (https://github.com/bio2bel) and PyPI.{\textless}/p{\textgreater}{\textless}h3{\textgreater}Conclusions{\textless}/h3{\textgreater} {\textless}p{\textgreater}The philosophy of Bio2BEL encourages reproducibility, accessibility, and democratization of biological databases. We present several applications of Bio2BEL packages including their ability to support the curation of pathway mappings, integration of pathway databases, and machine learning applications.{\textless}/p{\textgreater}{\textless}h3{\textgreater}Tweet{\textless}/h3{\textgreater} {\textless}p{\textgreater}A suite of independent Python packages for downloading, parsing, warehousing, and converting multi-modal and multi-scale biological databases to Biological Expression Language{\textless}/p{\textgreater}},
	language = {en},
	urldate = {2019-07-25},
	journal = {bioRxiv},
	author = {Hoyt, Charles Tapley and Domingo-Fernández, Daniel and Mubeen, Sarah and Llaó, Josep Marin and Konotopez, Andrej and Ebeling, Christian and Birkenbihl, Colin and Muslu, Özlem and English, Bradley and Müller, Simon and Lacerda, Mauricio Pio de and Ali, Mehdi and Colby, Scott and Türei, Dénes and Palacio-Escat, Nicolàs and Hofmann-Apitius, Martin},
	month = may,
	year = {2019},
	pages = {631812},
}

@misc{mccormick_word2vec_nodate,
	title = {Word2Vec {Tutorial} - {The} {Skip}-{Gram} {Model}},
	url = {http://www.mccormickml.com},
	urldate = {2019-07-25},
	author = {McCormick, Chris}
}

@article{muslu_guiltytargets:_2019,
	title = {{GuiltyTargets}: {Prioritization} of {Novel} {Therapeutic} {Targets} with {Deep} {Network} {Representation} {Learning}},
	copyright = {© 2019, Posted by Cold Spring Harbor Laboratory. This pre-print is available under a Creative Commons License (Attribution 4.0 International), CC BY 4.0, as described at http://creativecommons.org/licenses/by/4.0/},
	shorttitle = {{GuiltyTargets}},
	url = {https://www.biorxiv.org/content/10.1101/521161v1},
	doi = {10.1101/521161},
	abstract = {{\textless}h3{\textgreater}Abstract{\textless}/h3{\textgreater} {\textless}p{\textgreater}The majority of clinical trial failures are caused by low efficacy of investigated drugs, often due to a poor choice of target protein. Computational prioritization approaches aim to support target selection by ranking candidate targets in the context of a given disease. We propose a novel target prioritization approach, GuiltyTargets, which relies on deep network representation learning of a genome-wide protein-protein interaction network annotated with disease-specific differential gene expression and uses positive-unlabeled machine learning for candidate ranking. We evaluated our approach on six diseases of different types (cancer, metabolic, neurodegenerative) within a 10 times repeated 5-fold stratified cross-validation and achieved AUROC values between 0.92 - 0.94, significantly outperforming a previous approach, which relies on manually engineered topological features. Moreover, we showed that GuiltyTargets allows for target repositioning across related disease areas. Applying GuiltyTargets to Alzheimer’s disease resulted into a number of highly ranked candidates that are currently discussed as targets in the literature. Interestingly, one (COMT) is also the target of an approved drug (Tolcapone) for Parkinson’s disease, highlighting the potential for target repositioning of our method.{\textless}/p{\textgreater}{\textless}h3{\textgreater}Availability{\textless}/h3{\textgreater} {\textless}p{\textgreater}The GuiltyTargets Python package is available on PyPI and all code used for analysis can be found under the MIT License at https://github.com/GuiltyTargets.{\textless}/p{\textgreater}{\textless}h3{\textgreater}Author summary{\textless}/h3{\textgreater} {\textless}p{\textgreater}Many drug candidates fail in clinical trials due to low efficacy. One of the reasons is the choice of the wrong target protein, i.e. perturbation of the protein does not effectively modulate the disease phenotype on a molecular level. In consequence many patients do not demonstrate a clear response to the drug candidate. Traditionally, targets are selected based on evidence from the literature and follow-up experiments. However, this process is very labor intensive and often biased by subjective choices. Computational tools could help a more rational and unbiased choice of target proteins and thus increase the chance of drug discovery programs. In this work we propose a novel machine learning based method for target candidate ranking. The method (GuiltyTargets) captures properties of known targets to learn a ranking of candidates. GuiltyTargets compares favorably against existing machine learning based target prioritization methods and allowed us to propose novel targets for Alzheimer’s disease.{\textless}/p{\textgreater}},
	language = {en},
	urldate = {2019-07-25},
	journal = {bioRxiv},
	author = {Muslu, Özlem and Hoyt, Charles Tapley and Hofmann-Apitius, Martin and Fröhlich, Holger},
	month = jan,
	year = {2019},
	pages = {521161}
}

@article{braschi_genenames.org:_2019,
	title = {Genenames.org: the {HGNC} and {VGNC} resources in 2019},
	volume = {47},
	issn = {1362-4962},
	shorttitle = {Genenames.org},
	doi = {10.1093/nar/gky930},
	abstract = {The HUGO Gene Nomenclature Committee (HGNC) based at EMBL's European Bioinformatics Institute (EMBL-EBI) assigns unique symbols and names to human genes. There are over 40 000 approved gene symbols in our current database of which over 19 000 are for protein-coding genes. The Vertebrate Gene Nomenclature Committee (VGNC) was established in 2016 to assign standardized nomenclature in line with human for vertebrate species that lack their own nomenclature committees. The VGNC initially assigned nomenclature for over 15000 protein-coding genes in chimpanzee. We have extended this process to other vertebrate species, naming over 14000 protein-coding genes in cow and dog and over 13 000 in horse to date. Our HGNC website https://www.genenames.org has undergone a major design update, simplifying the homepage to provide easy access to our search tools and making the site more mobile friendly. Our gene families pages are now known as 'gene groups' and have increased in number to over 1200, with nearly half of all named genes currently assigned to at least one gene group. This article provides an overview of our online data and resources, focusing on our work over the last two years.},
	language = {eng},
	number = {D1},
	journal = {Nucleic Acids Research},
	author = {Braschi, Bryony and Denny, Paul and Gray, Kristian and Jones, Tamsin and Seal, Ruth and Tweedie, Susan and Yates, Bethan and Bruford, Elspeth},
	month = jan,
	year = {2019},
	pmid = {30304474},
	pmcid = {PMC6324057},
	pages = {D786--D792}
}

@article{noauthor_uniprot:_2019,
	title = {{UniProt}: a worldwide hub of protein knowledge},
	volume = {47},
	issn = {0305-1048},
	shorttitle = {{UniProt}},
	url = {https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6323992/},
	doi = {10.1093/nar/gky1049},
	abstract = {The UniProt Knowledgebase is a collection of sequences and annotations for over 120 million proteins across all branches of life. Detailed annotations extracted from the literature by expert curators have been collected for over half a million of these proteins. These annotations are supplemented by annotations provided by rule based automated systems, and those imported from other resources. In this article we describe significant updates that we have made over the last 2 years to the resource. We have greatly expanded the number of Reference Proteomes that we provide and in particular we have focussed on improving the number of viral Reference Proteomes. The UniProt website has been augmented with new data visualizations for the subcellular localization of proteins as well as their structure and interactions. UniProt resources are available under a CC-BY (4.0) license via the web at https://www.uniprot.org/.},
	number = {Database issue},
	urldate = {2019-07-26},
	journal = {Nucleic Acids Research},
	month = jan,
	year = {2019},
	pmid = {30395287},
	pmcid = {PMC6323992},
	pages = {D506--D515}
}

@article{roberts_pubmed_2001,
	title = {{PubMed} {Central}: {The} {GenBank} of the published literature},
	volume = {98},
	issn = {0027-8424},
	shorttitle = {{PubMed} {Central}},
	url = {https://www.ncbi.nlm.nih.gov/pmc/articles/PMC33354/},
	number = {2},
	urldate = {2019-07-26},
	journal = {Proceedings of the National Academy of Sciences of the United States of America},
	author = {Roberts, Richard J.},
	month = jan,
	year = {2001},
	pmid = {11209037},
	pmcid = {PMC33354},
	pages = {381--382}
}

@article{noauthor_gene_2008,
	title = {The {Gene} {Ontology} project in 2008},
	volume = {36},
	issn = {0305-1048},
	url = {https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2238979/},
	doi = {10.1093/nar/gkm883},
	abstract = {The Gene Ontology (GO) project (http://www.geneontology.org/) provides a set of structured, controlled vocabularies for community use in annotating genes, gene products and sequences (also see http://www.sequenceontology.org/). The ontologies have been extended and refined for several biological areas, and improvements to the structure of the ontologies have been implemented. To improve the quantity and quality of gene product annotations available from its public repository, the GO Consortium has launched a focused effort to provide comprehensive and detailed annotation of orthologous genes across a number of ‘reference’ genomes, including human and several key model organisms. Software developments include two releases of the ontology-editing tool OBO-Edit, and improvements to the AmiGO browser interface.},
	number = {Database issue},
	urldate = {2019-07-26},
	journal = {Nucleic Acids Research},
	month = jan,
	year = {2008},
	pmid = {17984083},
	pmcid = {PMC2238979},
	pages = {D440--D444}
}

@inproceedings{baralis_exploring_2008,
	title = {Exploring {Heterogeneous} {Biological} {Data} {Sources}},
	doi = {10.1109/DEXA.2008.116},
	abstract = {Research activity in the life science area is becoming increasingly data intensive. Huge amounts of highly heterogeneous data, including high throughput experiment results, publication collections, and clinical records are generated at a fast pace by researchers all over the world. The capability of correlating heterogeneous information stored in separated data repositories is a compelling, yet currently unsatisfied, need for bioinformatics scientists. Developed systems address this issue by building knowledge repositories for specific bioinformatics sub-domains such as protein-protein interaction or array expression analysis. In this paper we present an overview of heterogeneous biological data sources and discuss the many difficulties faced by biological data querying and analysis.},
	booktitle = {2008 19th {International} {Workshop} on {Database} and {Expert} {Systems} {Applications}},
	author = {Baralis, E. and Fiori, A.},
	month = sep,
	year = {2008},
	keywords = {Proteins, Bioinformatics, array expression analysis, Arrays, bioinformatics, biological data analysis, biological data querying, biological databases, Biological information theory, Biology, biology computing, data analysis, data repositories, Databases, heterogeneous biological data sources, knowledge repositories, life science area, Ontologies, protein-protein interaction, proteins, query processing, querying, research activity},
	pages = {647--651}
}

@article{huang_recommending_2011,
	title = {Recommending {MeSH} terms for annotating biomedical articles},
	volume = {18},
	issn = {1067-5027},
	url = {https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3168302/},
	doi = {10.1136/amiajnl-2010-000055},
	abstract = {Background
Due to the high cost of manual curation of key aspects from the scientific literature, automated methods for assisting this process are greatly desired. Here, we report a novel approach to facilitate MeSH indexing, a challenging task of assigning MeSH terms to MEDLINE citations for their archiving and retrieval.

Methods
Unlike previous methods for automatic MeSH term assignment, we reformulate the indexing task as a ranking problem such that relevant MeSH headings are ranked higher than those irrelevant ones. Specifically, for each document we retrieve 20 neighbor documents, obtain a list of MeSH main headings from neighbors, and rank the MeSH main headings using ListNet–a learning-to-rank algorithm. We trained our algorithm on 200 documents and tested on a previously used benchmark set of 200 documents and a larger dataset of 1000 documents.

Results
Tested on the benchmark dataset, our method achieved a precision of 0.390, recall of 0.712, and mean average precision (MAP) of 0.626. In comparison to the state of the art, we observe statistically significant improvements as large as 39\% in MAP (p-value {\textless}0.001). Similar significant improvements were also obtained on the larger document set.

Conclusion
Experimental results show that our approach makes the most accurate MeSH predictions to date, which suggests its great potential in making a practical impact on MeSH indexing. Furthermore, as discussed the proposed learning framework is robust and can be adapted to many other similar tasks beyond MeSH indexing in the biomedical domain. All data sets are available at: http://www.ncbi.nlm.nih.gov/CBBresearch/Lu/indexing.},
	number = {5},
	urldate = {2019-07-26},
	journal = {Journal of the American Medical Informatics Association : JAMIA},
	author = {Huang, Minlie and Névéol, Aurélie and Lu, Zhiyong},
	year = {2011},
	pmid = {21613640},
	pmcid = {PMC3168302},
	pages = {660--667}
}

@misc{noauthor_introduction_nodate,
	type = {Technical {Documentation}},
	title = {Introduction to {MeSH}},
	copyright = {Public Domain},
	url = {https://www.nlm.nih.gov/mesh/introduction.html},
	language = {eng},
	urldate = {2019-07-26}
}

@article{bodenreider_unified_2004,
	title = {The {Unified} {Medical} {Language} {System} ({UMLS}): integrating biomedical terminology},
	volume = {32},
	issn = {0305-1048},
	shorttitle = {The {Unified} {Medical} {Language} {System} ({UMLS})},
	url = {https://www.ncbi.nlm.nih.gov/pmc/articles/PMC308795/},
	doi = {10.1093/nar/gkh061},
	abstract = {The Unified Medical Language System (http://umlsks.nlm.nih.gov) is a repository of biomedical vocabularies developed by the US National Library of Medicine. The UMLS integrates over 2 million names for some 900 000 concepts from more than 60 families of biomedical vocabularies, as well as 12 million relations among these concepts. Vocabularies integrated in the UMLS Metathesaurus include the NCBI taxonomy, Gene Ontology, the Medical Subject Headings (MeSH), OMIM and the Digital Anatomist Symbolic Knowledge Base. UMLS concepts are not only inter-related, but may also be linked to external resources such as GenBank. In addition to data, the UMLS includes tools for customizing the Metathesaurus (MetamorphoSys), for generating lexical variants of concept names (lvg) and for extracting UMLS concepts from text (MetaMap). The UMLS knowledge sources are updated quarterly. All vocabularies are available at no fee for research purposes within an institution, but UMLS users are required to sign a license agreement. The UMLS knowledge sources are distributed on CD-ROM and by FTP.},
	number = {Database issue},
	urldate = {2019-07-26},
	journal = {Nucleic Acids Research},
	author = {Bodenreider, Olivier},
	month = jan,
	year = {2004},
	pmid = {14681409},
	pmcid = {PMC308795},
	pages = {D267--D270}
}

@article{hamilton_representation_nodate,
	title = {Representation {Learning} on {Graphs}: {Methods} and {Applications}},
	abstract = {Machine learning on graphs is an important and ubiquitous task with applications ranging from drug design to friendship recommendation in social networks. The primary challenge in this domain is finding a way to represent, or encode, graph structure so that it can be easily exploited by machine learning models. Traditionally, machine learning approaches relied on user-defined heuristics to extract features encoding structural information about a graph (e.g., degree statistics or kernel functions). However, recent years have seen a surge in approaches that automatically learn to encode graph structure into low-dimensional embeddings, using techniques based on deep learning and nonlinear dimensionality reduction. Here we provide a conceptual review of key advancements in this area of representation learning on graphs, including matrix factorization-based methods, random-walk based algorithms, and graph convolutional networks. We review methods to embed individual nodes as well as approaches to embed entire (sub)graphs. In doing so, we develop a unified framework to describe these recent approaches, and we highlight a number of important applications and directions for future work.},
	language = {en},
	author = {Hamilton, William L and Ying, Rex and Leskovec, Jure},
	pages = {23}
}

@incollection{dancik_properties_2013,
	address = {Dordrecht},
	title = {Properties of {Biological} {Networks}},
	isbn = {978-94-007-6803-1},
	url = {https://doi.org/10.1007/978-94-007-6803-1_5},
	abstract = {Relationships in biological systems are frequently represented as networks with the goal of abstracting a system’s components to nodes and connections between them. While such representations allow modeling and analysis using abstract computational methods, there are certain aspects of such modeling that are particularly important for biological networks. We explore features that are deemed necessary for living and evolving organisms and reflect the evolutionary origins of biological networks. Biological networks are robust to random alterations of their nodes and connections yet may be vulnerable to attacks targeting essential genes. Biological systems are dynamic and modular, and so are their network representations. Comparisons of biological networks across species can reveal conserved and evolved regions and shed light on evolutionary events and processes. It is important to understand networks as a whole, as significant insights might emerge from the network approach that cannot be attributed to properties of the nodes alone. Network-based approaches have a potential to significantly increase our understanding of biological systems and consequently, our understanding and treatment of human diseases.},
	language = {en},
	urldate = {2019-07-26},
	booktitle = {Systems {Biology}: {Integrative} {Biology} and {Simulation} {Tools}},
	publisher = {Springer Netherlands},
	author = {Dančík, Vlado and Basu, Amrita and Clemons, Paul},
	editor = {Prokop, Aleš and Csukás, Béla},
	year = {2013},
	doi = {10.1007/978-94-007-6803-1_5},
	keywords = {Biological networks, Emergent network properties, Network conservation, Network dynamics, Network medicine, Network modularity, Network robustness},
	pages = {129--178}
}

@misc{lobo_auc:_2008,
	title = {{AUC}: a misleading measure of the performance of predictive distribution models},
	shorttitle = {{AUC}},
	url = {https://onlinelibrary.wiley.com/doi/abs/10.1111/j.1466-8238.2007.00358.x},
	language = {en},
	urldate = {2019-08-06},
	journal = {Global Ecology and Biogeography},
	author = {Lobo, Jorge M. and Jiménez‐Valverde, Alberto and Real, Raimundo},
	month = mar,
	year = {2008},
	doi = {10.1111/j.1466-8238.2007.00358.x}
}

@article{nickel_review_2016,
	title = {A {Review} of {Relational} {Machine} {Learning} for {Knowledge} {Graphs}},
	volume = {104},
	issn = {0018-9219, 1558-2256},
	url = {http://arxiv.org/abs/1503.00759},
	doi = {10.1109/JPROC.2015.2483592},
	abstract = {Relational machine learning studies methods for the statistical analysis of relational, or graph-structured, data. In this paper, we provide a review of how such statistical models can be “trained” on large knowledge graphs, and then used to predict new facts about the world (which is equivalent to predicting new edges in the graph). In particular, we discuss two different kinds of statistical relational models, both of which can scale to massive datasets. The first is based on tensor factorization methods and related latent variable models. The second is based on mining observable patterns in the graph. We also show how to combine these latent and observable models to get improved modeling power at decreased computational cost. Finally, we discuss how such statistical models of graphs can be combined with text-based information extraction methods for automatically constructing knowledge graphs from the Web. In particular, we discuss Google’s Knowledge Vault project.},
	language = {en},
	number = {1},
	urldate = {2019-08-23},
	journal = {Proceedings of the IEEE},
	author = {Nickel, Maximilian and Murphy, Kevin and Tresp, Volker and Gabrilovich, Evgeniy},
	month = jan,
	year = {2016},
	note = {arXiv: 1503.00759},
	keywords = {Computer Science - Machine Learning, Statistics - Machine Learning},
	pages = {11--33}
}

@article{akiba_optuna:_2019,
	title = {Optuna: {A} {Next}-generation {Hyperparameter} {Optimization} {Framework}},
	shorttitle = {Optuna},
	url = {http://arxiv.org/abs/1907.10902},
	abstract = {The purpose of this study is to introduce new design-criteria for next-generation hyperparameter optimization software. The criteria we propose include (1) define-by-run API that allows users to construct the parameter search space dynamically, (2) efficient implementation of both searching and pruning strategies, and (3) easy-to-setup, versatile architecture that can be deployed for various purposes, ranging from scalable distributed computing to light-weight experiment conducted via interactive interface. In order to prove our point, we will introduce Optuna, an optimization software which is a culmination of our effort in the development of a next generation optimization software. As an optimization software designed with define-by-run principle, Optuna is particularly the first of its kind. We will present the design-techniques that became necessary in the development of the software that meets the above criteria, and demonstrate the power of our new design through experimental results and real world applications. Our software is available under the MIT license (https://github.com/pfnet/optuna/).},
	urldate = {2019-08-23},
	journal = {arXiv:1907.10902 [cs, stat]},
	author = {Akiba, Takuya and Sano, Shotaro and Yanase, Toshihiko and Ohta, Takeru and Koyama, Masanori},
	month = jul,
	year = {2019},
	note = {arXiv: 1907.10902},
	keywords = {Computer Science - Machine Learning, Statistics - Machine Learning}
}

@inproceedings{tsitsulin_verse:_2018,
	address = {Lyon, France},
	title = {{VERSE}: {Versatile} {Graph} {Embeddings} from {Similarity} {Measures}},
	isbn = {978-1-4503-5639-8},
	shorttitle = {{VERSE}},
	url = {http://dl.acm.org/citation.cfm?doid=3178876.3186120},
	doi = {10.1145/3178876.3186120},
	abstract = {Embedding a web-scale information network into a low-dimensional vector space facilitates tasks such as link prediction, classification, and visualization. Past research has addressed the problem of extracting such embeddings by adopting methods from words to graphs, without defining a clearly comprehensible graph-related objective. Yet, as we show, the objectives used in past works implicitly utilize similarity measures among graph nodes. In this paper, we carry the similarity orientation of previous works to its logical conclusion; we propose VERtex Similarity Embeddings (VERSE), a simple, versatile, and memory-efficient method that derives graph embeddings explicitly calibrated to preserve the distributions of a selected vertex-to-vertex similarity measure. VERSE learns such embeddings by training a single-layer neural network. While its default, scalable version does so via sampling similarity information, we also develop a variant using the full information per vertex. Our experimental study on standard benchmarks and real-world datasets demonstrates that VERSE, instantiated with diverse similarity measures, outperforms state-of-the-art methods in terms of precision and recall in major data mining tasks and supersedes them in time and space efficiency, while the scalable sampling-based variant achieves equally good results as the nonscalable full variant.},
	language = {en},
	urldate = {2019-08-26},
	booktitle = {Proceedings of the 2018 {World} {Wide} {Web} {Conference} on {World} {Wide} {Web}  - {WWW} '18},
	publisher = {ACM Press},
	author = {Tsitsulin, Anton and Mottin, Davide and Karras, Panagiotis and Müller, Emmanuel},
	year = {2018},
	pages = {539--548}
}

@article{yuan_sne:_2017,
	title = {{SNE}: {Signed} {Network} {Embedding}},
	shorttitle = {{SNE}},
	url = {http://arxiv.org/abs/1703.04837},
	abstract = {Several network embedding models have been developed for unsigned networks. However, these models based on skip-gram cannot be applied to signed networks because they can only deal with one type of link. In this paper, we present our signed network embedding model called SNE. Our SNE adopts the log-bilinear model, uses node representations of all nodes along a given path, and further incorporates two signed-type vectors to capture the positive or negative relationship of each edge along the path. We conduct two experiments, node classification and link prediction, on both directed and undirected signed networks and compare with four baselines including a matrix factorization method and three state-of-the-art unsigned network embedding models. The experimental results demonstrate the effectiveness of our signed network embedding.},
	urldate = {2019-08-26},
	journal = {arXiv:1703.04837 [cs]},
	author = {Yuan, Shuhan and Wu, Xintao and Xiang, Yang},
	month = mar,
	year = {2017},
	note = {arXiv: 1703.04837},
	keywords = {Computer Science - Social and Information Networks}
}

@article{vital-lopez_tutorial_2012,
	title = {Tutorial on biological networks: {Tutorial} on biological networks},
	volume = {2},
	issn = {19424787},
	shorttitle = {Tutorial on biological networks},
	url = {http://doi.wiley.com/10.1002/widm.1061},
	doi = {10.1002/widm.1061},
	language = {en},
	number = {4},
	urldate = {2019-08-27},
	journal = {Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery},
	author = {Vital-Lopez, Francisco G. and Memišević, Vesna and Dutta, Bhaskar},
	month = jul,
	year = {2012},
	pages = {298--325}
}

@article{zhu_getting_2007,
	title = {Getting connected: analysis and principles of biological networks},
	volume = {21},
	issn = {0890-9369},
	shorttitle = {Getting connected},
	doi = {10.1101/gad.1528707},
	abstract = {The execution of complex biological processes requires the precise interaction and regulation of thousands of molecules. Systematic approaches to study large numbers of proteins, metabolites, and their modification have revealed complex molecular networks. These biological networks are significantly different from random networks and often exhibit ubiquitous properties in terms of their structure and organization. Analyzing these networks provides novel insights in understanding basic mechanisms controlling normal cellular processes and disease pathologies.},
	language = {eng},
	number = {9},
	journal = {Genes \& Development},
	author = {Zhu, Xiaowei and Gerstein, Mark and Snyder, Michael},
	month = may,
	year = {2007},
	pmid = {17473168},
	keywords = {Humans, Proteins, Disease, Systems Biology, Animals, Evolution, Molecular, Models, Biological, Models, Genetic, Mutation, Phosphorylation, Protein Interaction Mapping, Transcription Factors},
	pages = {1010--1024}
}

@article{arrowsmith_phase_2011,
	title = {Phase {II} failures: 2008–2010},
	volume = {10},
	issn = {1474-1776, 1474-1784},
	shorttitle = {Phase {II} failures},
	url = {http://www.nature.com/articles/nrd3439},
	doi = {10.1038/nrd3439},
	language = {en},
	number = {5},
	urldate = {2019-08-28},
	journal = {Nature Reviews Drug Discovery},
	author = {Arrowsmith, John},
	month = may,
	year = {2011},
	pages = {328--329}
}

@article{arrowsmith_phase_2013,
	title = {Phase {II} and {Phase} {III} attrition rates 2011–2012},
	volume = {12},
	issn = {1474-1776, 1474-1784},
	url = {http://www.nature.com/articles/nrd4090},
	doi = {10.1038/nrd4090},
	language = {en},
	number = {8},
	urldate = {2019-08-28},
	journal = {Nature Reviews Drug Discovery},
	author = {Arrowsmith, John and Miller, Philip},
	month = aug,
	year = {2013},
	pages = {569--569}
}

@article{yue_graph_2019,
	title = {Graph {Embedding} on {Biomedical} {Networks}: {Methods}, {Applications}, and {Evaluations}},
	shorttitle = {Graph {Embedding} on {Biomedical} {Networks}},
	url = {http://arxiv.org/abs/1906.05017},
	abstract = {Motivation: Graph embedding learning which aims to automatically learn low-dimensional node representations has drawn increasing attention in recent years. To date, most recent graph embedding methods are mainly evaluated on social and information networks and have not yet been comprehensively studied on biomedical networks under systematic experiments and analyses. On the other hand, for a variety of biomedical network analysis tasks, traditional techniques such as matrix factorization (which can be seen as one type of graph embedding methods) have shown promising results, and hence there is a need to systematically evaluate the more recent graph embedding methods (e.g., random walk-based and neural network-based) in terms of their usability and potential to further the state-of-the-art.},
	language = {en},
	urldate = {2019-08-28},
	journal = {arXiv:1906.05017 [cs]},
	author = {Yue, Xiang and Wang, Zhen and Huang, Jingong and Parthasarathy, Srinivasan and Moosavinasab, Soheil and Huang, Yungui and Lin, Simon M. and Zhang, Wen and Zhang, Ping and Sun, Huan},
	month = jun,
	year = {2019},
	note = {arXiv: 1906.05017},
	keywords = {Computer Science - Social and Information Networks, Computer Science - Machine Learning}
}

@article{jankovic_parkinsons_2008,
	title = {Parkinson's disease: clinical features and diagnosis},
	volume = {79},
	issn = {0022-3050},
	shorttitle = {Parkinson's disease},
	url = {http://jnnp.bmj.com/cgi/doi/10.1136/jnnp.2007.131045},
	doi = {10.1136/jnnp.2007.131045},
	language = {en},
	number = {4},
	urldate = {2019-08-28},
	journal = {Journal of Neurology, Neurosurgery \& Psychiatry},
	author = {Jankovic, J},
	month = apr,
	year = {2008},
	pages = {368--376}
}

@article{el-saifi_quetiapine_2016,
	title = {Quetiapine safety in older adults: a systematic literature review},
	volume = {41},
	issn = {02694727},
	shorttitle = {Quetiapine safety in older adults},
	url = {http://doi.wiley.com/10.1111/jcpt.12357},
	doi = {10.1111/jcpt.12357},
	language = {en},
	number = {1},
	urldate = {2019-08-28},
	journal = {Journal of Clinical Pharmacy and Therapeutics},
	author = {El-Saifi, N. and Moyle, W. and Jones, C. and Tuffaha, H.},
	month = feb,
	year = {2016},
	pages = {7--18}
}

@article{rabey_effect_2007,
	title = {Effect of quetiapine in psychotic {Parkinson}'s disease patients: a double-blind labeled study of 3 months' duration},
	volume = {22},
	issn = {0885-3185},
	shorttitle = {Effect of quetiapine in psychotic {Parkinson}'s disease patients},
	doi = {10.1002/mds.21116},
	abstract = {This double-blind randomized study examined the effect of quetiapine (QTP) on drug-induced psychosis (DIP) in Parkinson's disease (PD). Conventional antipsychotic drugs are associated with adverse extrapyramidal effects. QTP is a new atypical antipsychotic drug used in the treatment of psychosis in PD. A total of 58 consecutive psychotic PD patients (mean age, 75 +/- 8.3 years; mean disease duration, 10.5 +/- 6.4 years; 29 with dementia) were randomly assigned to 2 groups: 30 were treated with QTP (mean dose, 119.2 +/- 56.4 mg) and 28 received placebo for 3 months. The motor part of the Unified Parkinson's Disease Rating Scale, the Brief Psychiatric Rating Scale, the Mini-Mental State Examination, the Hamilton Rating Scale for Depression, the Epworth Sleepiness Score, and the Clinical Global Impression Scale were administered before and during the study. No significant difference was found between the groups in all parameters. There were 32 PD patients (55\%) completed the 3-month study (15 [26\%] QTP and 17 [29\%] placebo). Treatment was interrupted in 15 patients in the QTP and 11 in the placebo groups. This double-blind study did not show a beneficial effect of QTP for the treatment of DIP in PD. The high rate of withdrawal probably influenced the results. Larger double-blind studies are required.},
	language = {eng},
	number = {3},
	journal = {Movement Disorders: Official Journal of the Movement Disorder Society},
	author = {Rabey, Jose M. and Prokhorov, Tatiana and Miniovitz, Ala and Dobronevsky, Eugenia and Klein, Colin},
	month = feb,
	year = {2007},
	pmid = {17034006},
	keywords = {Double-Blind Method, Humans, Male, Aged, Aged, 80 and over, Antipsychotic Agents, Brief Psychiatric Rating Scale, Dibenzothiazepines, Female, Follow-Up Studies, Outcome Assessment (Health Care), Parkinson Disease, Psychotic Disorders, Quetiapine Fumarate, Severity of Illness Index},
	pages = {313--318}
}

@article{shotbolt_quetiapine_2010,
	title = {Quetiapine in the treatment of psychosis in {Parkinson}'s disease},
	volume = {3},
	issn = {1756-2864},
	doi = {10.1177/1756285610389656},
	abstract = {Psychosis (delusions and/or hallucinations) is a common nonmotor feature of Parkinson's disease (PD). Use of the older 'typical' antipsychotic drugs led to worsening of motor symptoms. The introduction of 'atypical' antipsychotics opened up a range of therapeutic options. These agents include clozapine, risperidone, olanzapine, aripiprazole and quetiapine. All have been used to treat psychosis in PD with varying success. Clozapine is the only drug with proven efficacy. We review the evidence for the efficacy of quetiapine. Eight open-label studies have assessed quetiapine use in 191 patients, with improvement in psychosis recorded in 152 (80\%). In addition to the open-label studies, there have been two single-blind, randomized trials comparing quetiapine and clozapine. These studies suggest that quetiapine has similar efficacy to clozapine in controlling psychosis. Following the promising results of the open-label and clozapine comparison studies, five randomized, controlled trials (RCTs) have been performed to further establish the efficacy of quetiapine. Unfortunately, the results have been disappointing. The only positive placebo-controlled study excluded patients with delusions, which seem to be harder to treat than hallucinations. The four negative RCTs discussed seriously undermine the evidence from the open-label studies. The differences in design and interpretation of the RCTs emphasizes the need for further large, well-controlled trials, using strict inclusion criteria, appropriate psychosis rating scales, carer input and clinical significance. Currently, many physicians continue to cautiously offer a trial of low-dose quetiapine empirically. Clozapine should be considered in patients who can tolerate the required blood monitoring.},
	language = {eng},
	number = {6},
	journal = {Therapeutic Advances in Neurological Disorders},
	author = {Shotbolt, Paul and Samuel, Mike and David, Anthony},
	month = nov,
	year = {2010},
	pmid = {21179595},
	pmcid = {PMC3002640},
	keywords = {antipsychotics, Parkinson’s disease, psychosis, quetiapine},
	pages = {339--350}
}

@article{desmarais_quetiapine_2016,
	title = {Quetiapine for {Psychosis} in {Parkinson} {Disease} and {Neurodegenerative} {Parkinsonian} {Disorders}: {A} {Systematic} {Review}},
	volume = {29},
	issn = {0891-9887},
	shorttitle = {Quetiapine for {Psychosis} in {Parkinson} {Disease} and {Neurodegenerative} {Parkinsonian} {Disorders}},
	doi = {10.1177/0891988716640378},
	abstract = {We performed a systematic review of randomized controlled trials to assess the high-level evidence regarding the role of quetiapine in the treatment of psychosis in patients with neurodegenerative parkinsonian disorders. Studies were included in the qualitative review if they (1) enrolled participants with diagnosis of Parkinson disease, Lewy body dementia, or any other neurodegenerative parkinsonian disorders; (2) assessed the efficacy of quetiapine; and (3) evaluated psychotic and motor outcomes using validated tools. Of the 341 manuscripts identified, 7 studies fulfilled our inclusion criteria. The studies' risk of bias was considered low. A total of 241 participants enrolled in these trials. Heterogeneity was high due to inclusion criteria, user definitions, assessment tools, and study design. Although not causing any motor deterioration, quetiapine failed to significantly reduce psychotic symptoms compared to placebo when objectively assessed on the Brief Psychotic Rating Scale, the most frequently reported scale in these studies. High loss to follow-up and dropout rates as well as significant improvement in psychotic symptoms in the placebo groups may have affected measurements of possible positive medication effects.},
	language = {eng},
	number = {4},
	journal = {Journal of Geriatric Psychiatry and Neurology},
	author = {Desmarais, Philippe and Massoud, Fadi and Filion, Josée and Nguyen, Quoc Dinh and Bajsarowicz, Paulina},
	month = jul,
	year = {2016},
	pmid = {27056066},
	keywords = {Humans, Male, Middle Aged, Aged, Antipsychotic Agents, Dibenzothiazepines, Female, Parkinson Disease, Psychotic Disorders, Quetiapine Fumarate, antipsychotics, psychosis, quetiapine, Lewy body dementia, Lewy Body Disease, Parkinson disease, parkinsonism, Randomized Controlled Trials as Topic},
	pages = {227--236}
}

@article{warner_role_2018,
	title = {The {Role} of {Atomoxetine} for {Parkinson} {Disease}–{Related} {Executive} {Dysfunction}: {A} {Systematic} {Review}},
	issn = {0271-0749},
	shorttitle = {The {Role} of {Atomoxetine} for {Parkinson} {Disease}–{Related} {Executive} {Dysfunction}},
	url = {http://Insights.ovid.com/crossref?an=00004714-900000000-98640},
	doi = {10.1097/JCP.0000000000000963},
	language = {en},
	urldate = {2019-08-28},
	journal = {Journal of Clinical Psychopharmacology},
	author = {Warner, Carly B. and Ottman, Andreina A. and Brown, Jamie N.},
	month = oct,
	year = {2018},
	pages = {1}
}

@article{borchert_atomoxetine_2016,
	title = {Atomoxetine {Enhances} {Connectivity} of {Prefrontal} {Networks} in {Parkinson}'s {Disease}},
	volume = {41},
	issn = {1740-634X},
	doi = {10.1038/npp.2016.18},
	abstract = {Cognitive impairment is common in Parkinson's disease (PD), but often not improved by dopaminergic treatment. New treatment strategies targeting other neurotransmitter deficits are therefore of growing interest. Imaging the brain at rest ('task-free') provides the opportunity to examine the impact of a candidate drug on many of the brain networks that underpin cognition, while minimizing task-related performance confounds. We test this approach using atomoxetine, a selective noradrenaline reuptake inhibitor that modulates the prefrontal cortical activity and can facilitate some executive functions and response inhibition. Thirty-three patients with idiopathic PD underwent task-free fMRI. Patients were scanned twice in a double-blind, placebo-controlled crossover design, following either placebo or 40-mg oral atomoxetine. Seventy-six controls were scanned once without medication to provide normative data. Seed-based correlation analyses were used to measure changes in functional connectivity, with the right inferior frontal gyrus (IFG) a critical region for executive function. Patients on placebo had reduced connectivity relative to controls from right IFG to dorsal anterior cingulate cortex and to left IFG and dorsolateral prefrontal cortex. Atomoxetine increased connectivity from the right IFG to the dorsal anterior cingulate. In addition, the atomoxetine-induced change in connectivity from right IFG to dorsolateral prefrontal cortex was proportional to the change in verbal fluency, a simple index of executive function. The results support the hypothesis that atomoxetine may restore prefrontal networks related to executive functions. We suggest that task-free imaging can support translational pharmacological studies of new drug therapies and provide evidence for engagement of the relevant neurocognitive systems.},
	language = {eng},
	number = {8},
	journal = {Neuropsychopharmacology: Official Publication of the American College of Neuropsychopharmacology},
	author = {Borchert, Robin J. and Rittman, Timothy and Passamonti, Luca and Ye, Zheng and Sami, Saber and Jones, Simon P. and Nombela, Cristina and Vázquez Rodríguez, Patricia and Vatansever, Deniz and Rae, Charlotte L. and Hughes, Laura E. and Robbins, Trevor W. and Rowe, James B.},
	year = {2016},
	pmid = {26837463},
	pmcid = {PMC4856878},
	keywords = {Cross-Over Studies, Double-Blind Method, Humans, Male, Middle Aged, Aged, Female, Parkinson Disease, Adrenergic Uptake Inhibitors, Atomoxetine Hydrochloride, Brain Mapping, Magnetic Resonance Imaging, Neural Pathways, Prefrontal Cortex},
	pages = {2171--2177}
}

@article{ye_improving_2015,
	title = {Improving response inhibition in {Parkinson}'s disease with atomoxetine},
	volume = {77},
	issn = {1873-2402},
	doi = {10.1016/j.biopsych.2014.01.024},
	abstract = {BACKGROUND: Dopaminergic drugs remain the mainstay of Parkinson's disease therapy but often fail to improve cognitive problems such as impulsivity. This may be due to the loss of other neurotransmitters, including noradrenaline, which is linked to impulsivity and response inhibition. We therefore examined the effect of the selective noradrenaline reuptake inhibitor atomoxetine on response inhibition in a stop-signal paradigm.
METHODS: This pharmacological functional magnetic resonance imaging study used a double-blinded randomized crossover design with low-frequency inhibition trials distributed among frequent Go trials. Twenty-one patients received 40 mg atomoxetine or placebo. Control subjects were tested on no-drug. The effects of disease and drug on behavioral performance, regional brain activity, and functional connectivity were analyzed using general linear models. Anatomical connectivity was examined using diffusion-weighted imaging.
RESULTS: Patients with Parkinson's disease had longer stop-signal reaction times, less stop-related activation in the right inferior frontal gyrus (RIFG), and weaker functional connectivity between the RIFG and striatum compared with control subjects. Atomoxetine enhanced stop-related RIFG activation in proportion to disease severity. Although there was no overall behavioral benefit from atomoxetine, analyses of individual differences revealed that enhanced response inhibition by atomoxetine was associated with increased RIFG activation and functional frontostriatal connectivity. Improved performance was more likely in patients with higher structural frontostriatal connectivity.
CONCLUSIONS: This study suggests that enhanced prefrontal cortical activation and frontostriatal connectivity by atomoxetine may improve response inhibition in Parkinson's disease. These results point the way to new stratified clinical trials of atomoxetine to treat impulsivity in selected patients with Parkinson's disease.},
	language = {eng},
	number = {8},
	journal = {Biological Psychiatry},
	author = {Ye, Zheng and Altena, Ellemarije and Nombela, Cristina and Housden, Charlotte R. and Maxwell, Helen and Rittman, Timothy and Huddleston, Chelan and Rae, Charlotte L. and Regenthal, Ralf and Sahakian, Barbara J. and Barker, Roger A. and Robbins, Trevor W. and Rowe, James B.},
	month = apr,
	year = {2015},
	pmid = {24655598},
	pmcid = {PMC4384955},
	keywords = {Double-Blind Method, Humans, Male, Middle Aged, Aged, Aged, 80 and over, Female, Parkinson Disease, Parkinson’s disease, Adrenergic Uptake Inhibitors, Atomoxetine Hydrochloride, Neural Pathways, Atomoxetine, Brain, Choice Behavior, Cognition Disorders, Cross-Sectional Studies, Image Processing, Computer-Assisted, Impulsivity, Inhibition (Psychology), Neuropsychological Tests, Noradrenaline, Oxygen, Reaction Time, Response inhibition, SSRT},
	pages = {740--748}
}

@article{dennehy_efficacy_2003,
	title = {The efficacy of olanzapine monotherapy for acute hypomania or mania in an outpatient setting},
	volume = {18},
	issn = {0268-1315},
	doi = {10.1097/01.yic.0000062801.74434.25},
	abstract = {Randomized controlled trials have demonstrated the efficacy of olanzapine for treating acute mania or depression symptoms in patients with bipolar disorder. We aimed to evaluate the effectiveness of this medication in more usual care outpatient settings. A consecutive series of 15 patients entered an open, uncontrolled 8-week trial of olanzapine monotherapy. Inclusion criteria included significant hypomanic or manic symptoms greater than or equal to 15 on the Young Mania Rating Scale and no psychotic symptoms. The majority of patients experienced significant decreases in mania ratings and more limited improvement on depression ratings. Most patients reported adverse events consistent with other studies, but few discontinued due to these complaints. This case series highlights the individual variation in response to a proven medication. Furthermore, it highlights that those medications effective at one end of the mood spectrum may not be equally or simultaneously effective with other symptoms, emphasizing the complexity of treating bipolar illness.},
	language = {eng},
	number = {3},
	journal = {International Clinical Psychopharmacology},
	author = {Dennehy, Ellen B. and Doyle, Kimberly and Suppes, Trisha},
	month = may,
	year = {2003},
	pmid = {12702892},
	keywords = {Administration, Oral, Humans, Male, Middle Aged, Treatment Outcome, Antipsychotic Agents, Female, Severity of Illness Index, Adult, Affect, Benzodiazepines, Bipolar Disorder, Olanzapine, Outpatients, Pirenzepine},
	pages = {143--145}
}

@incollection{thomas_olanzapine_2019,
	address = {Treasure Island (FL)},
	title = {Olanzapine},
	copyright = {Copyright © 2019, StatPearls Publishing LLC.},
	url = {http://www.ncbi.nlm.nih.gov/books/NBK532903/},
	abstract = {Olanzapine is a second generation (atypical) antipsychotic medication. The FDA has approved this medication for the following conditions:},
	language = {eng},
	urldate = {2019-08-31},
	booktitle = {{StatPearls}},
	publisher = {StatPearls Publishing},
	author = {Thomas, Kristina and Saadabadi, Abdolreza},
	year = {2019},
	pmid = {30422498}
}

@article{gupta_reversible_2014,
	title = {Reversible bilateral sensori-neural hearing loss due to olanzapine in a male suffering from bipolar affective disorder},
	volume = {46},
	issn = {0253-7613},
	url = {https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4118547/},
	doi = {10.4103/0253-7613.135966},
	abstract = {An elderly male with Bipolar Affective Disorder (BPAD) developed reversible ototoxicity, manifesting as bilateral sensory-neural hearing loss (SNHL) with administration of olanzapine.},
	number = {4},
	urldate = {2019-08-31},
	journal = {Indian Journal of Pharmacology},
	author = {Gupta, Sumit Kumar and Shwetank, Bansal},
	year = {2014},
	pmid = {25097292},
	pmcid = {PMC4118547},
	pages = {453--454}
}

@article{david_effects_2000,
	title = {The effects of olanzapine, risperidone, and haloperidol on plasma prolactin levels in patients with schizophrenia},
	volume = {22},
	issn = {0149-2918},
	doi = {10.1016/S0149-2918(00)80086-7},
	abstract = {BACKGROUND: There is relatively little comparative information on elevations in plasma prolactin level (PRL) with conventional versus novel antipsychotic agents.
OBJECTIVE: This paper examines the comparative effects on PRL of olanzapine, risperidone, and haloperidol based on data from 3 multicenter, double-blind, randomized clinical trials. Magnitude of response, dose dependency, time course, effects of sex and age, and response to switching from haloperidol to olanzapine are assessed.
METHODS: The effects of olanzapine, risperidone, and haloperidol on PRL were assessed in patients with schizophrenia or related psychoses participating in 3 double-blind clinical trials: (1) a 6-week acute trial comparing olanzapine 5 to 20 mg/d (n = 1,336) and haloperidol 5 to 20 mg/d (n = 660), with a 1-year, open-label olanzapine extension for responders; (2) a 54-week study comparing olanzapine 5 to 20 mg/d (n = 21), risperidone 4 to 10 mg/d (n = 21), and haloperidol 5 to 20 mg/d (n = 23) in early illness; and (3) a 28-week study comparing olanzapine 10 to 20 mg/d (n = 172) and risperidone 4 to 12 mg/d (n = 167).
RESULTS: PRL elevations were significantly greater with risperidone than with either olanzapine or haloperidol in study 2. and significantly greater than with olanzapine in study 3 (all, P {\textless} 0.001). PRL elevations were significantly greater with haloperidol than with olanzapine in study 1 (P {\textless} 0.001 ). A dose-response relationship was not consistently confirmed with any of the drug treatments. Risperidone-associated PRL elevations peaked relatively early in treatment. In haloperidol- and risperidone-treated patients, the mean change in PRL was greater in women than in men. PRL decreased significantly when treatment was switched from haloperidol to olanzapine.
CONCLUSIONS: This side-by-side analysis of 3 independent studies suggests that with the 3 antipsychotic drugs studied, PRL is elevated moderately by olanzapine (mean change, 1-4 ng/mL), intermediately by haloperidol (mean change, approximately 17 ng/mL), and strongly by risperidone (mean change, 45-80 ng/mL). No consistent dose-response relationship was observed, and the time course and sex-dependency of the response differed between the 3 agents. Patients with haloperidol-induced hyperprolactinemia may benefit from a switch to olanzapine. Long-term studies examining the health consequences of chronic hyperprolactinemia during antipsychotic treatment are needed.},
	language = {eng},
	number = {9},
	journal = {Clinical Therapeutics},
	author = {David, S. R. and Taylor, C. C. and Kinon, B. J. and Breier, A.},
	month = sep,
	year = {2000},
	pmid = {11048906},
	keywords = {Cross-Over Studies, Humans, Antipsychotic Agents, Benzodiazepines, Olanzapine, Pirenzepine, Dose-Response Relationship, Drug, Haloperidol, Prolactin, Risperidone, Schizophrenia},
	pages = {1085--1096}
}

@article{chen_prolactin_2009,
	title = {Prolactin {Levels} in {Olanzapine} {Treatment} {Correlate} {With} {Positive} {Symptoms} of {Schizophrenia}: {Results} {From} an {Open}-{Label}, {Flexible}-{Dose} {Study}},
	volume = {11},
	issn = {1523-5998},
	shorttitle = {Prolactin {Levels} in {Olanzapine} {Treatment} {Correlate} {With} {Positive} {Symptoms} of {Schizophrenia}},
	url = {https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2660160/},
	abstract = {Objective:
This study was designed to investigate the relationship between the treatment effect of olanzapine and the serum prolactin level in schizophrenia and to investigate the factors that may act as predictors of response for olanzapine treatment.

Method:
Sixty patients who met the DSM-IV criteria for schizophrenia were included in the study. None of the patients were drug-naive, and they were given olanzapine in a flexible dose of 10–30 mg/day for 3 months after a 7-day drug washout period. Serum prolactin levels were measured at baseline (after drug washout) and at months 1, 2, and 3 during olanzapine treatment. A psychiatrist performed monthly ratings of symptoms using the Positive and Negative Syndrome Scale Manual (PANSS Manual). The Generalized Estimating Equations-I was used for data correlation analysis. Data were gathered from July 2005 to July 2006.

Results:
In general, the serum prolactin level was decreased in schizophrenia patients with olanzapine treatment, although the difference is not statistically significant (p = .974, p = .246, and p = .363 for the first, second, and third months, respectively). There was a close relationship between the improvement in positive symptoms and the change in serum prolactin levels before and after olanzapine treatment (p = .002). Moreover, the serum prolactin level also had a positive association with female gender (p = .008). The present study demonstrated no significant correlation between serum prolactin level, MAOA polymorphism, and DRD4 genotype.

Conclusion:
This finding suggests that the serum prolactin level may be a useful biological marker to predict the effectiveness of antipsychotics in schizophrenia.},
	number = {1},
	urldate = {2019-08-31},
	journal = {Primary Care Companion to The Journal of Clinical Psychiatry},
	author = {Chen, Yi-Lung and Cheng, Ting-Sheng and Lung, For-Wey},
	year = {2009},
	pmid = {19333405},
	pmcid = {PMC2660160},
	pages = {16--20}
}

@article{canuso_olanzapine_1998,
	title = {Olanzapine {Use} in {Women} {With} {Antipsychotic}-{Induced} {Hyperprolactinemia}},
	volume = {155},
	issn = {0002-953X},
	url = {https://ajp.psychiatryonline.org/doi/full/10.1176/ajp.155.10.1458},
	doi = {10.1176/ajp.155.10.1458},
	number = {10},
	urldate = {2019-08-31},
	journal = {American Journal of Psychiatry},
	author = {Canuso, Carla M. and Hanau, Michael and Jhamb, Kristin K. and Green, Alan I.},
	month = oct,
	year = {1998},
	pages = {1458--1458}
}

@article{haslett_can_2002,
	title = {Can olanzapine be implicated in causing serotonin syndrome?},
	volume = {56},
	issn = {1440-1819},
	url = {https://onlinelibrary.wiley.com/doi/abs/10.1046/j.1440-1819.2002.01050.x},
	doi = {10.1046/j.1440-1819.2002.01050.x},
	abstract = {The present paper describes a case of serotonin syndrome (SS), which developed in a patient with bipolar affective disorder after the addition of olanzapine to her regimen of lithium and citalopram. This appears to be the first report that implicates olanzapine with SS. Clinicians should be aware of the risk of SS when adding atypical antipsychotics, such as olanzapine, to serotonergic agents.},
	language = {en},
	number = {5},
	urldate = {2019-08-31},
	journal = {Psychiatry and Clinical Neurosciences},
	author = {Haslett, Christopher David and Kumar, Shailesh},
	year = {2002},
	keywords = {atypical antipsychotics, olanzapine, serotonin syndrome},
	pages = {533--535}
}

@article{volpi-abadie_serotonin_2013,
	title = {Serotonin {Syndrome}},
	volume = {13},
	issn = {1524-5012},
	url = {https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3865832/},
	abstract = {Background
Serotonin syndrome is a potentially life-threatening syndrome that is precipitated by the use of serotonergic drugs and overactivation of both the peripheral and central postsynaptic 5HT-1A and, most notably, 5HT-2A receptors. This syndrome consists of a combination of mental status changes, neuromuscular hyperactivity, and autonomic hyperactivity. Serotonin syndrome can occur via the therapeutic use of serotonergic drugs alone, an intentional overdose of serotonergic drugs, or classically, as a result of a complex drug interaction between two serotonergic drugs that work by different mechanisms. A multitude of drug combinations can result in serotonin syndrome.

Methods
This review describes the presentation and management of serotonin syndrome and discusses the drugs and interactions that can precipitate this syndrome with the goal of making physicians more alert and aware of this potentially fatal yet preventable syndrome.

Conclusion
Many commonly used medications have proven to be the culprits of serotonin syndrome. Proper education and awareness about serotonin syndrome will improve the accuracy of diagnosis and promote the institution of the appropriate treatment that may prevent significant morbidity and mortality.},
	number = {4},
	urldate = {2019-08-31},
	journal = {The Ochsner Journal},
	author = {Volpi-Abadie, Jacqueline and Kaye, Adam M. and Kaye, Alan David},
	year = {2013},
	pmid = {24358002},
	pmcid = {PMC3865832},
	pages = {533--540}
}

@article{chepure_olanzapine-induced_2017,
	title = {Olanzapine-induced {Psoriasis}},
	volume = {39},
	issn = {0253-7176},
	url = {https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5733436/},
	doi = {10.4103/IJPSYM.IJPSYM_21_17},
	abstract = {Olanzapine is a second-generation antipsychotic drug mainly used in the treatment of schizophrenia. It has several side effects including weight gain, hypercholesterolemia, and hyperglycemia, but dermatological side effects are rarely reported. We report a rare case of olanzapine-induced psoriasis.},
	number = {6},
	urldate = {2019-09-01},
	journal = {Indian Journal of Psychological Medicine},
	author = {Chepure, Ashish Hanmantrao and Ungratwar, Apurva Karmveer},
	year = {2017},
	pmid = {29284819},
	pmcid = {PMC5733436},
	pages = {811--812}
}

@article{lykouras_manic_nodate,
	title = {Manic symptoms induced by olanzapine},
	volume = {11},
	issn = {0924-977X},
	url = {https://www.academia.edu/18071575/Manic_symptoms_induced_by_olanzapine},
	abstract = {Manic symptoms induced by olanzapine},
	language = {en},
	urldate = {2019-09-01},
	journal = {European Neuropsychopharmacology},
	author = {Lykouras, Lefteris and Gournellis, Rossetos and Angelopoulos, Elias},
	pages = {97--98}
}

@article{popovic_revisiting_2015,
	title = {Revisiting loxapine: a systematic review},
	volume = {14},
	issn = {1744-859X},
	shorttitle = {Revisiting loxapine},
	url = {https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4391595/},
	doi = {10.1186/s12991-015-0053-3},
	abstract = {Loxapine is an antipsychotic used in psychiatry for over 40 years with a well-established profile. Loxapine is a dibenzoxazepine tricyclic antipsychotic agent, available for oral, intramuscular and inhalatory administration. In the light of the recent approval by the regulatory agencies of inhaled loxapine for use in the acute treatment of mild-to-moderate agitation in adults affected with schizophrenia or bipolar disorder, this article aims to critically review the available literature on loxapine, irrespective of its formulation., This review examines the efficacy and tolerability of the various formulations of loxapine in the treatment of agitation and aggression in patients affected with schizophrenia, bipolar disorder and other psychiatric conditions., A comprehensive and systematic literature search of PubMed/MEDLINE was conducted, and relevant pharmacodynamic and pharmacokinetic data was included. The findings from the literature were critically reviewed and synthesized., The available data suggests that the antipsychotic efficacy of loxapine is similar to the efficacy of other typical or atypical antipsychotics, with an adverse effects profile comparable to that of the typical antipsychotics at high doses for chronic treatment. As an acute treatment in agitation associated with schizophrenia or bipolar disorder, inhaled loxapine was developed as an innovative and rapid option which appears to be efficacious and tolerable.},
	urldate = {2019-09-01},
	journal = {Annals of General Psychiatry},
	author = {Popovic, Dina and Nuss, Philippe and Vieta, Eduard},
	month = apr,
	year = {2015},
	pmid = {25859275},
	pmcid = {PMC4391595}
}

@article{dietz_evidence_2013,
	title = {Evidence for the use of {Levomepromazine} for symptom control in the palliative care setting: a systematic review},
	volume = {12},
	issn = {1472-684X},
	shorttitle = {Evidence for the use of {Levomepromazine} for symptom control in the palliative care setting},
	url = {https://doi.org/10.1186/1472-684X-12-2},
	doi = {10.1186/1472-684X-12-2},
	abstract = {Levomepromazine is an antipsychotic drug that is used clinically for a variety of distressing symptoms in palliative and end-of-life care. We undertook a systematic review based on the question “What is the published evidence for the use of levomepromazine in palliative symptom control?”.},
	number = {1},
	urldate = {2019-09-01},
	journal = {BMC Palliative Care},
	author = {Dietz, Isabel and Schmitz, Andrea and Lampey, Ingrid and Schulz, Christian},
	month = jan,
	year = {2013},
	pages = {2}
}

@article{chow_treatment_1998,
	title = {Treatment of {Depression} in the {Patient} with {Parkinson}'s {Disease}},
	volume = {6},
	issn = {1095-1598},
	url = {https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5436796/},
	number = {11},
	urldate = {2019-09-01},
	journal = {Clinical geriatrics},
	author = {Chow, Tiffany W. and Cummings, Jeffrey L.},
	month = oct,
	year = {1998},
	pmid = {28529443},
	pmcid = {PMC5436796},
	pages = {34--46}
}

@article{chen_anxiety_2014,
	title = {Anxiety in {Parkinson}’s disease: identification and management},
	volume = {7},
	issn = {1756-2856},
	shorttitle = {Anxiety in {Parkinson}’s disease},
	url = {https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3886380/},
	doi = {10.1177/1756285613495723},
	abstract = {Anxiety disturbances are recognized as common psychiatric comorbidities in Parkinson’s disease (PD) and contribute to significant impairments in areas of cognitive, functional, motor and social performance. Anxiety in PD results in reduced quality of life, higher levels of care dependency and increased caregiver burden. Surprisingly, there is a paucity of treatment data. In one randomized, controlled study, bromazepam was found to be effective for anxiety in PD. However, usage of benzodiazepines in the PD population is limited by potential risk of confusion and falls. There are no controlled studies of selective serotonin reuptake inhibitors (SSRIs) for anxiety in PD. However, results from uncontrolled studies suggest that SSRIs are effective for anxiety in PD, although in these studies anxiety outcomes were secondary. This review underscores that, given the high prevalence of anxiety disturbances in PD, there is a significant paucity of treatment data for this population. Additional studies are warranted. In the meantime, clinicians should rely on empiric assessments of known risks and putative benefits to guide treatment decisions. Cognitive and behavioral therapies (with or without pharmacotherapy) have demonstrated efficacy and warrant consideration. When feasible, a targeted and individualized multimodal approach utilizing psychotherapeutic interventions along with pharmacologic therapies should be considered.},
	number = {1},
	urldate = {2019-09-01},
	journal = {Therapeutic Advances in Neurological Disorders},
	author = {Chen, Jack J. and Marsh, Laura},
	month = jan,
	year = {2014},
	pmid = {24409202},
	pmcid = {PMC3886380},
	pages = {52--59}
}

@incollection{aronstam_muscarinic_2009,
	address = {Oxford},
	title = {Muscarinic {Receptors}: {Autonomic} {Neurons}},
	isbn = {978-0-08-045046-9},
	shorttitle = {Muscarinic {Receptors}},
	url = {http://www.sciencedirect.com/science/article/pii/B9780080450469006926},
	abstract = {Muscarinic receptors bind acetylcholine to transduce signals in the central nervous system, autonomic ganglia, smooth muscles, and parasympathetic organs. The five closely related receptor subtypes (M1–M5) are members of the class A (rhodopsin-like) superfamily of G-protein-coupled receptors. These receptors possess seven membrane-spanning domains and specific sites for glycosylation, phosphorylation, lipid modification, and interaction with heterotrimeric transducer G-proteins. M1, M3, and M5 receptors are efficiently coupled through Gq/11 proteins to phospholipase Cβ, producing the second messengers diacylglycerol and inositol triphosphate. M2 and M4 receptors inhibit adenylate cyclase through actions involving Gi. Our understanding of muscarinic systems has advanced by analysis of receptor gene knockouts.},
	urldate = {2019-09-01},
	booktitle = {Encyclopedia of {Neuroscience}},
	publisher = {Academic Press},
	author = {Aronstam, R. S. and Patil, P.},
	editor = {Squire, Larry R.},
	month = jan,
	year = {2009},
	doi = {10.1016/B978-008045046-9.00692-6},
	keywords = {Acetylcholine, Adenylate cyclase, Allosteric receptor regulation, Atropine, Cholinergic, G-protein-coupled receptor, Muscarinic receptor, Parasympathetic nervous system, Phospholipase C, Signal transduction, Synaptic transmission},
	pages = {1141--1149}
}

@incollection{kleinz_chapter_2008,
	address = {Edinburgh},
	title = {Chapter 4 - {The} pharmacology of the autonomic nervous system},
	isbn = {978-0-7020-2858-8},
	url = {http://www.sciencedirect.com/science/article/pii/B9780702028588500063},
	urldate = {2019-09-01},
	booktitle = {Small {Animal} {Clinical} {Pharmacology} ({Second} {Edition})},
	publisher = {W.B. Saunders},
	author = {Kleinz, Matthias J and Spence, Ian},
	editor = {Maddison, JILL E and Page, STEPHEN W and Church, DAVID B},
	month = jan,
	year = {2008},
	doi = {10.1016/B978-070202858-8.50006-3},
	pages = {59--82}
}

@article{jeon_role_2015,
	title = {The {Role} of {Muscarinic} {Receptors} in the {Pathophysiology} of {Mood} {Disorders}: {A} {Potential} {Novel} {Treatment}?},
	volume = {13},
	issn = {1570-159X},
	shorttitle = {The {Role} of {Muscarinic} {Receptors} in the {Pathophysiology} of {Mood} {Disorders}},
	url = {https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4759313/},
	doi = {10.2174/1570159X13666150612230045},
	abstract = {The central cholinergic system has been implicated in the pathophysiology of mood disorders. An imbalance in central cholinergic neurotransmitter activity has been proposed to contribute to the manic and depressive episodes typical of these disorders. Neuropharmacological studies into the effects of cholinergic agonists and antagonists on mood state have provided considerable support for this hypothesis. Furthermore, recent clinical studies have shown that the pan-CHRM antagonist, scopolamine, produces rapid-acting antidepressant effects in individuals with either major depressive disorder (MDD) or bipolar disorder (BPD), such as bipolar depression, contrasting the delayed therapeutic response of conventional mood stabilisers and antidepressants. This review presents recent data from neuroimaging, post-mortem and genetic studies supporting the involvement of muscarinic cholinergic receptors (CHRMs), particularly CHRM2, in the pathophysiology of MDD and BPD. Thus, novel drugs that selectively target CHRMs with negligible effects in the peripheral nervous system might produce more rapid and robust clinical improvement in patients with BPD and MDD.},
	number = {6},
	urldate = {2019-09-01},
	journal = {Current Neuropharmacology},
	author = {Jeon, Won Je and Dean, Brian and Scarr, Elizabeth and Gibbons, Andrew},
	month = dec,
	year = {2015},
	pmid = {26630954},
	pmcid = {PMC4759313},
	pages = {739--749}
}

@inproceedings{pennington_glove:_2014,
	address = {Doha, Qatar},
	title = {Glove: {Global} {Vectors} for {Word} {Representation}},
	shorttitle = {Glove},
	url = {http://aclweb.org/anthology/D14-1162},
	doi = {10.3115/v1/D14-1162},
	abstract = {Recent methods for learning vector space representations of words have succeeded in capturing fine-grained semantic and syntactic regularities using vector arithmetic, but the origin of these regularities has remained opaque. We analyze and make explicit the model properties needed for such regularities to emerge in word vectors. The result is a new global logbilinear regression model that combines the advantages of the two major model families in the literature: global matrix factorization and local context window methods. Our model efficiently leverages statistical information by training only on the nonzero elements in a word-word cooccurrence matrix, rather than on the entire sparse matrix or on individual context windows in a large corpus. The model produces a vector space with meaningful substructure, as evidenced by its performance of 75\% on a recent word analogy task. It also outperforms related models on similarity tasks and named entity recognition.},
	language = {en},
	urldate = {2019-09-01},
	booktitle = {Proceedings of the 2014 {Conference} on {Empirical} {Methods} in {Natural} {Language} {Processing} ({EMNLP})},
	publisher = {Association for Computational Linguistics},
	author = {Pennington, Jeffrey and Socher, Richard and Manning, Christopher},
	year = {2014},
	pages = {1532--1543}
}

@inproceedings{cao_grarep:_2015,
	title = {{GraRep}: {Learning} {Graph} {Representations} with {Global} {Structural} {Information}},
	shorttitle = {{GraRep}},
	doi = {10.1145/2806416.2806512},
	abstract = {In this paper, we present \{GraRep\}, a novel model for learning vertex representations of weighted graphs. This model learns low dimensional vectors to represent vertices appearing in a graph and, unlike existing work, integrates global structural information of the graph into the learning process. We also formally analyze the connections between our work and several previous research efforts, including the DeepWalk model of Perozzi et al. as well as the skip-gram model with negative sampling of Mikolov et al.
 We conduct experiments on a language network, a social network as well as a citation network and show that our learned global representations can be effectively used as features in tasks such as clustering, classification and visualization. Empirical results demonstrate that our representation significantly outperforms other state-of-the-art methods in such tasks.},
	booktitle = {{CIKM}},
	author = {Cao, Shaosheng and Lu, Wei and Xu, Qiongkai},
	year = {2015},
	keywords = {Experiment, Citation network, Cluster analysis, N-gram, Sampling (signal processing), Skip list, Social network, Statistical classification}
}

@article{liang_predicting_2019,
	title = {Predicting biomedical relationships using the knowledge and graph embedding cascade model},
	volume = {14},
	issn = {1932-6203},
	url = {https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6565371/},
	doi = {10.1371/journal.pone.0218264},
	abstract = {Advances in machine learning and deep learning methods, together with the increasing availability of large-scale pharmacological, genomic, and chemical datasets, have created opportunities for identifying potentially useful relationships within biochemical networks. Knowledge embedding models have been found to have value in detecting knowledge-based correlations among entities, but little effort has been made to apply them to networks of biochemical entities. This is because such networks tend to be unbalanced and sparse, and knowledge embedding models do not work well on them. However, to some extent, the shortcomings of knowledge embedding models can be compensated for if they are used in association with graph embedding. In this paper, we combine knowledge embedding and graph embedding to represent biochemical entities and their relations as dense and low-dimensional vectors. We build a cascade learning framework which incorporates semantic features from the knowledge embedding model, and graph features from the graph embedding model, to score the probability of linking. The proposed method performs noticeably better than the models with which it is compared. It predicted links and entities with an accuracy of 93\%, and its average hits@10 score has an average of 8.6\% absolute improvement compared with original knowledge embedding model, 1.1\% to 9.7\% absolute improvement compared with other knowledge and graph embedding algorithm. In addition, we designed a meta-path algorithm to detect path relations in the biomedical network. Case studies further verify the value of the proposed model in finding potential relationships between diseases, drugs, genes, treatments, etc. Amongst the findings of the proposed model are the suggestion that VDR (vitamin D receptor) may be linked to prostate cancer. This is backed by evidence from medical databases and published research, supporting the suggestion that our proposed model could be of value to biomedical researchers.},
	number = {6},
	urldate = {2019-09-01},
	journal = {PLoS ONE},
	author = {Liang, Xiaomin and Li, Daifeng and Song, Min and Madden, Andrew and Ding, Ying and Bu, Yi},
	month = jun,
	year = {2019},
	pmid = {31194807},
	pmcid = {PMC6565371}
}

@article{boughorbel_optimal_2017,
	title = {Optimal classifier for imbalanced data using {Matthews} {Correlation} {Coefficient} metric},
	volume = {12},
	issn = {1932-6203},
	url = {https://dx.plos.org/10.1371/journal.pone.0177678},
	doi = {10.1371/journal.pone.0177678},
	language = {en},
	number = {6},
	urldate = {2019-09-02},
	journal = {PLOS ONE},
	author = {Boughorbel, Sabri and Jarray, Fethi and El-Anbari, Mohammed},
	editor = {Zou, Quan},
	month = jun,
	year = {2017},
	pages = {e0177678}
}

@inproceedings{wang_unsupervised_2016,
	title = {Unsupervised {Learning} from {Noisy} {Networks} with {Applications} to {Hi}-{C} {Data}},
	abstract = {Complex networks play an important role in a plethora of disciplines in natural sciences. Cleaning up noisy observed networks, poses an important challenge in network analysis Existing methods utilize labeled data to alleviate the noise effect in the network. However, labeled data is usually expensive to collect while unlabeled data can be gathered cheaply. In this paper, we propose an optimization framework to mine useful structures from noisy networks in an unsupervised manner. The key feature of our optimization framework is its ability to utilize local structures as well as global patterns in the network. We extend our method to incorporate multi-resolution networks in order to add further resistance to high-levels of noise. We also generalize our framework to utilize partial labels to enhance the performance. We specifically focus our method on multi-resolution Hi-C data by recovering clusters of genomic regions that co-localize in 3D space. Additionally, we use Capture-C-generated partial labels to further denoise the Hi-C network. We empirically demonstrate the effectiveness of our framework in denoising the network and improving community detection results.},
	booktitle = {{NIPS}},
	author = {Wang, Bing and Zhu, Junjie and Pourshafeie, Armin and Ursu, Oana and Batzoglou, Serafim and Kundaje, Anshul},
	year = {2016},
	keywords = {Cluster analysis, Algorithm, Analysis of algorithms, Interaction network, K-means clustering, Mathematical optimization, Noise reduction, Product binning, Semi-supervised learning, Semiconductor industry, Unsupervised learning}
}

@article{amberger_omim.org:_2015,
	title = {{OMIM}.org: {Online} {Mendelian} {Inheritance} in {Man} ({OMIM}®), an online catalog of human genes and genetic disorders},
	volume = {43},
	issn = {1362-4962},
	shorttitle = {{OMIM}.org},
	doi = {10.1093/nar/gku1205},
	abstract = {Online Mendelian Inheritance in Man, OMIM(®), is a comprehensive, authoritative and timely research resource of curated descriptions of human genes and phenotypes and the relationships between them. The new official website for OMIM, OMIM.org (http://omim.org), was launched in January 2011. OMIM is based on the published peer-reviewed biomedical literature and is used by overlapping and diverse communities of clinicians, molecular biologists and genome scientists, as well as by students and teachers of these disciplines. Genes and phenotypes are described in separate entries and are given unique, stable six-digit identifiers (MIM numbers). OMIM entries have a structured free-text format that provides the flexibility necessary to describe the complex and nuanced relationships between genes and genetic phenotypes in an efficient manner. OMIM also has a derivative table of genes and genetic phenotypes, the Morbid Map. OMIM.org has enhanced search capabilities such as genome coordinate searching and thesaurus-enhanced search term options. Phenotypic series have been created to facilitate viewing genetic heterogeneity of phenotypes. Clinical synopsis features are enhanced with UMLS, Human Phenotype Ontology and Elements of Morphology terms and image links. All OMIM data are available for FTP download and through an API. MIMmatch is a novel outreach feature to disseminate updates and encourage collaboration.},
	language = {eng},
	number = {Database issue},
	journal = {Nucleic Acids Research},
	author = {Amberger, Joanna S. and Bocchini, Carol A. and Schiettecatte, François and Scott, Alan F. and Hamosh, Ada},
	month = jan,
	year = {2015},
	pmid = {25428349},
	pmcid = {PMC4383985},
	keywords = {Humans, Phenotype, Internet, Databases, Genetic, Genes, Genetic Diseases, Inborn},
	pages = {D789--798}
}

@article{rosse_motivation_1998,
	title = {Motivation and organizational principles for anatomical knowledge representation: the digital anatomist symbolic knowledge base},
	volume = {5},
	issn = {1067-5027},
	shorttitle = {Motivation and organizational principles for anatomical knowledge representation},
	doi = {10.1136/jamia.1998.0050017},
	abstract = {OBJECTIVE: Conceptualization of the physical objects and spaces that constitute the human body at the macroscopic level of organization, specified as a machine-parseable ontology that, in its human-readable form, is comprehensible to both expert and novice users of anatomical information.
DESIGN: Conceived as an anatomical enhancement of the UMLS Semantic Network and Metathesaurus, the anatomical ontology was formulated by specifying defining attributes and differentia for classes and subclasses of physical anatomical entities based on their partitive and spatial relationships. The validity of the classification was assessed by instantiating the ontology for the thorax. Several transitive relationships were used for symbolically modeling aspects of the physical organization of the thorax.
RESULTS: By declaring Organ as the macroscopic organizational unit of the body, and defining the entities that constitute organs and higher level entities constituted by organs, all anatomical entities could be assigned to one of three top level classes (Anatomical structure, Anatomical spatial entity and Body substance). The ontology accommodates both the systemic and regional (topographical) views of anatomy, as well as diverse clinical naming conventions of anatomical entities.
CONCLUSIONS: The ontology formulated for the thorax is extendible to microscopic and cellular levels, as well as to other body parts, in that its classes subsume essentially all anatomical entities that constitute the body. Explicit definitions of these entities and their relationships provide the first requirement for standards in anatomical concept representation. Conceived from an anatomical viewpoint, the ontology can be generalized and mapped to other biomedical domains and problem solving tasks that require anatomical knowledge.},
	language = {eng},
	number = {1},
	journal = {Journal of the American Medical Informatics Association: JAMIA},
	author = {Rosse, C. and Mejino, J. L. and Modayur, B. R. and Jakobovits, R. and Hinshaw, K. P. and Brinkley, J. F.},
	month = feb,
	year = {1998},
	pmid = {9452983},
	pmcid = {PMC61273},
	keywords = {Humans, Anatomy, Artificial Intelligence, Semantics, Terminology as Topic, Thorax, Unified Medical Language System, Vocabulary, Controlled},
	pages = {17--40}
}

@article{gene_ontology_consortium_gene_2015,
	title = {Gene {Ontology} {Consortium}: going forward},
	volume = {43},
	issn = {1362-4962},
	shorttitle = {Gene {Ontology} {Consortium}},
	doi = {10.1093/nar/gku1179},
	abstract = {The Gene Ontology (GO; http://www.geneontology.org) is a community-based bioinformatics resource that supplies information about gene product function using ontologies to represent biological knowledge. Here we describe improvements and expansions to several branches of the ontology, as well as updates that have allowed us to more efficiently disseminate the GO and capture feedback from the research community. The Gene Ontology Consortium (GOC) has expanded areas of the ontology such as cilia-related terms, cell-cycle terms and multicellular organism processes. We have also implemented new tools for generating ontology terms based on a set of logical rules making use of templates, and we have made efforts to increase our use of logical definitions. The GOC has a new and improved web site summarizing new developments and documentation, serving as a portal to GO data. Users can perform GO enrichment analysis, and search the GO for terms, annotations to gene products, and associated metadata across multiple species using the all-new AmiGO 2 browser. We encourage and welcome the input of the research community in all biological areas in our continued effort to improve the Gene Ontology.},
	language = {eng},
	number = {Database issue},
	journal = {Nucleic Acids Research},
	author = {{Gene Ontology Consortium}},
	month = jan,
	year = {2015},
	pmid = {25428369},
	pmcid = {PMC4383973},
	keywords = {Internet, Databases, Genetic, Cell Cycle, Gene Ontology, Molecular Sequence Annotation},
	pages = {D1049--1056}
}

@inproceedings{ji_knowledge_2015,
	address = {Beijing, China},
	title = {Knowledge {Graph} {Embedding} via {Dynamic} {Mapping} {Matrix}},
	url = {https://www.aclweb.org/anthology/P15-1067},
	doi = {10.3115/v1/P15-1067},
	urldate = {2019-09-02},
	booktitle = {Proceedings of the 53rd {Annual} {Meeting} of the {Association} for {Computational} {Linguistics} and the 7th {International} {Joint} {Conference} on {Natural} {Language} {Processing} ({Volume} 1: {Long} {Papers})},
	publisher = {Association for Computational Linguistics},
	author = {Ji, Guoliang and He, Shizhu and Xu, Liheng and Liu, Kang and Zhao, Jun},
	month = jul,
	year = {2015},
	pages = {687--696}
}

@article{gruning_software_2019,
	title = {Software engineering for scientific big data analysis},
	volume = {8},
	issn = {2047-217X},
	url = {https://academic.oup.com/gigascience/article/doi/10.1093/gigascience/giz054/5497810},
	doi = {10.1093/gigascience/giz054},
	language = {en},
	number = {5},
	urldate = {2019-09-02},
	journal = {GigaScience},
	author = {Grüning, Björn A and Lampa, Samuel and Vaudel, Marc and Blankenberg, Daniel},
	month = may,
	year = {2019}
}

@misc{charles_tapley_hoyt_bio2bel/sider_2018,
	title = {bio2bel/sider v0.0.1},
	url = {https://zenodo.org/record/1882884#.XW2O7SgzZPZ},
	abstract = {A Bio2BEL package for the Side Effect Resource (SIDER; http://sideeffects.embl.de/)},
	urldate = {2019-09-02},
	publisher = {Zenodo},
	author = {Charles Tapley Hoyt},
	month = dec,
	year = {2018},
	doi = {10.5281/zenodo.1882884}
}

@misc{charles_tapley_hoyt_bio2bel/drugbank_2018,
	title = {bio2bel/drugbank v0.0.1},
	url = {https://zenodo.org/record/1243727#.XW2TASgzZPZ},
	abstract = {Converts DrugBank to BEL},
	urldate = {2019-09-02},
	publisher = {Zenodo},
	author = {Charles Tapley Hoyt},
	month = may,
	year = {2018},
	doi = {10.5281/zenodo.1243727}
}

@inproceedings{perozzi_deepwalk:_2014,
	address = {New York, New York, USA},
	title = {{DeepWalk}: online learning of social representations},
	isbn = {978-1-4503-2956-9},
	shorttitle = {{DeepWalk}},
	url = {http://dl.acm.org/citation.cfm?doid=2623330.2623732},
	doi = {10.1145/2623330.2623732},
	language = {en},
	urldate = {2019-09-05},
	booktitle = {Proceedings of the 20th {ACM} {SIGKDD} international conference on {Knowledge} discovery and data mining - {KDD} '14},
	publisher = {ACM Press},
	author = {Perozzi, Bryan and Al-Rfou, Rami and Skiena, Steven},
	year = {2014},
	pages = {701--710},
}

@article{butina_unsupervised_1999,
	title = {Unsupervised {Data} {Base} {Clustering} {Based} on {Daylight}'s {Fingerprint} and {Tanimoto} {Similarity}: {A} {Fast} and {Automated} {Way} {To} {Cluster} {Small} and {Large} {Data} {Sets}},
	volume = {39},
	issn = {0095-2338},
	shorttitle = {Unsupervised {Data} {Base} {Clustering} {Based} on {Daylight}'s {Fingerprint} and {Tanimoto} {Similarity}},
	url = {https://pubs.acs.org/doi/10.1021/ci9803381},
	doi = {10.1021/ci9803381},
	language = {en},
	number = {4},
	urldate = {2019-09-09},
	journal = {Journal of Chemical Information and Computer Sciences},
	author = {Butina, Darko},
	month = jul,
	year = {1999},
	pages = {747--750}
}

@article{ezzat_drug-target_2017,
	title = {Drug-{Target} {Interaction} {Prediction} with {Graph} {Regularized} {Matrix} {Factorization}},
	volume = {14},
	issn = {1557-9964},
	doi = {10.1109/TCBB.2016.2530062},
	abstract = {Experimental determination of drug-target interactions is expensive and time-consuming. Therefore, there is a continuous demand for more accurate predictions of interactions using computational techniques. Algorithms have been devised to infer novel interactions on a global scale where the input to these algorithms is a drug-target network (i.e., a bipartite graph where edges connect pairs of drugs and targets that are known to interact). However, these algorithms had difficulty predicting interactions involving new drugs or targets for which there are no known interactions (i.e., "orphan" nodes in the network). Since data usually lie on or near to low-dimensional non-linear manifolds, we propose two matrix factorization methods that use graph regularization in order to learn such manifolds. In addition, considering that many of the non-occurring edges in the network are actually unknown or missing cases, we developed a preprocessing step to enhance predictions in the "new drug" and "new target" cases by adding edges with intermediate interaction likelihood scores. In our cross validation experiments, our methods achieved better results than three other state-of-the-art methods in most cases. Finally, we simulated some "new drug" and "new target" cases and found that GRMF predicted the left-out interactions reasonably well.},
	language = {eng},
	number = {3},
	journal = {IEEE/ACM transactions on computational biology and bioinformatics},
	author = {Ezzat, Ali and Zhao, Peilin and Wu, Min and Li, Xiao-Li and Kwoh, Chee-Keong},
	month = jun,
	year = {2017},
	pmid = {26890921},
	keywords = {Algorithms, Computational Biology, Databases, Chemical, Drug Delivery Systems, Drug Discovery, Machine Learning, Pharmaceutical Preparations, Proteins, Reproducibility of Results},
	pages = {646--656}
}


@article{zeng_probability-based_2017,
	title = {Probability-based collaborative filtering model for predicting gene–disease associations},
	volume = {10},
	issn = {1755-8794},
	url = {https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5751590/},
	doi = {10.1186/s12920-017-0313-y},
}


@article{yamanishi_dinies:_2014,
	title = {{DINIES}: drug–target interaction network inference engine based on supervised analysis},
	volume = {42},
	issn = {0305-1048},
	shorttitle = {{DINIES}},
	url = {https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4086078/},
	doi = {10.1093/nar/gku337},
	abstract = {DINIES (drug–target interaction network inference engine based on supervised analysis) is a web server for predicting unknown drug–target interaction networks from various types of biological data (e.g. chemical structures, drug side effects, amino acid sequences and protein domains) in the framework of supervised network inference. The originality of DINIES lies in prediction with state-of-the-art machine learning methods, in the integration of heterogeneous biological data and in compatibility with the KEGG database. The DINIES server accepts any ‘profiles’ or precalculated similarity matrices (or ‘kernels’) of drugs and target proteins in tab-delimited file format. When a training data set is submitted to learn a predictive model, users can select either known interaction information in the KEGG DRUG database or their own interaction data. The user can also select an algorithm for supervised network inference, select various parameters in the method and specify weights for heterogeneous data integration. The server can provide integrative analyses with useful components in KEGG, such as biological pathways, functional hierarchy and human diseases. DINIES (http://www.genome.jp/tools/dinies/) is publicly available as one of the genome analysis tools in GenomeNet.},
	number = {Web Server issue},
	urldate = {2019-09-10},
	journal = {Nucleic Acids Research},
	author = {Yamanishi, Yoshihiro and Kotera, Masaaki and Moriya, Yuki and Sawada, Ryusuke and Kanehisa, Minoru and Goto, Susumu},
	month = jul,
	year = {2014},
	pmid = {24838565},
	pmcid = {PMC4086078},
	pages = {W39--W45}
}

@article{abdelaziz_large-scale_2017,
	title = {Large-scale structural and textual similarity-based mining of knowledge graph to predict drug–drug interactions},
	volume = {44},
	issn = {15708268},
	url = {https://linkinghub.elsevier.com/retrieve/pii/S157082681730029X},
	doi = {10.1016/j.websem.2017.06.002},
	language = {en},
	urldate = {2019-09-10},
	journal = {Journal of Web Semantics},
	author = {Abdelaziz, Ibrahim and Fokoue, Achille and Hassanzadeh, Oktie and Zhang, Ping and Sadoghi, Mohammad},
	month = may,
	year = {2017},
	pages = {104--117}
}

@article{wang_predicting_2017,
	title = {Predicting {Rich} {Drug}-{Drug} {Interactions} via {Biomedical} {Knowledge} {Graphs} and {Text} {Jointly} {Embedding}},
	url = {http://arxiv.org/abs/1712.08875},
	abstract = {Minimizing adverse reactions caused by drug-drug interactions has always been a momentous research topic in clinical pharmacology. Detecting all possible interactions through clinical studies before a drug is released to the market is a demanding task. The power of big data is opening up new approaches to discover various drug-drug interactions. However, these discoveries contain a huge amount of noise and provide knowledge bases far from complete and trustworthy ones to be utilized. Most existing studies focus on predicting binary drug-drug interactions between drug pairs but ignore other interactions. In this paper, we propose a novel framework, called PRD, to predict drug-drug interactions. The framework uses the graph embedding that can overcome data incompleteness and sparsity issues to achieve multiple DDI label prediction. First, a large-scale drug knowledge graph is generated from different sources. Then, the knowledge graph is embedded with comprehensive biomedical text into a common low dimensional space. Finally, the learned embeddings are used to efficiently compute rich DDI information through a link prediction process. To validate the effectiveness of the proposed framework, extensive experiments were conducted on real-world datasets. The results demonstrate that our model outperforms several state-of-the-art baseline methods in terms of capability and accuracy.},
	urldate = {2019-09-10},
	journal = {arXiv:1712.08875 [cs]},
	author = {Wang, Meng},
	month = dec,
	year = {2017},
	note = {arXiv: 1712.08875},
	keywords = {Computer Science - Artificial Intelligence},
	annote = {Comment: This article has been withdrawn by arXiv administrators due to an unresolvable authorship dispute}
}

@inproceedings{zhao_contextcare:_2017,
	address = {Melbourne, Australia},
	title = {{ContextCare}: {Incorporating} {Contextual} {Information} {Networks} to {Representation} {Learning} on {Medical} {Forum} {Data}},
	isbn = {978-0-9992411-0-3},
	shorttitle = {{ContextCare}},
	url = {https://www.ijcai.org/proceedings/2017/489},
	doi = {10.24963/ijcai.2017/489},
	abstract = {Online users have generated a large amount of health-related data on medical forums and search engines. However, exploiting these rich data for orienting patient online and assisting medical checkup offline is nontrivial due to the sparseness of existing symptom-disease links, which caused by the natural and chatty expressions of symptoms. In this paper, we propose a novel and general representation learning method CONTEXTCARE for human generated health-related data, which learns latent relationship between symptoms and diseases from the symptom-disease diagnosis network for disease prediction, disease category prediction and disease clustering. To alleviate the network sparseness, CONTEXTCARE adopts regularizations from rich contextual information networks including a symptom co-occurrence network and a disease evolution network. Extensive experiments on medical forum data demonstrate that CONTEXTCARE outperforms the state-of-the-art methods in respects.},
	language = {en},
	urldate = {2019-09-10},
	booktitle = {Proceedings of the {Twenty}-{Sixth} {International} {Joint} {Conference} on {Artificial} {Intelligence}},
	publisher = {International Joint Conferences on Artificial Intelligence Organization},
	author = {Zhao, Stan and Jiang, Meng and Yuan, Quan and Qin, Bing and Liu, Ting and Zhai, ChengXiang},
	month = aug,
	year = {2017},
	pages = {3497--3503}
}

@inproceedings{cheng_risk_2016,
	title = {Risk {Prediction} with {Electronic} {Health} {Records}: {A} {Deep} {Learning} {Approach}},
	isbn = {978-1-61197-434-8},
	shorttitle = {Risk {Prediction} with {Electronic} {Health} {Records}},
	url = {https://epubs.siam.org/doi/10.1137/1.9781611974348.49},
	doi = {10.1137/1.9781611974348.49},
	language = {en},
	urldate = {2019-09-10},
	booktitle = {Proceedings of the 2016 {SIAM} {International} {Conference} on {Data} {Mining}},
	publisher = {Society for Industrial and Applied Mathematics},
	author = {Cheng, Yu and Wang, Fei and Zhang, Ping and Hu, Jianying},
	month = jun,
	year = {2016},
	pages = {432--440}
}

@article{zitnik_modeling_2018,
	title = {Modeling polypharmacy side effects with graph convolutional networks},
	volume = {34},
	issn = {1367-4803},
	url = {https://academic.oup.com/bioinformatics/article/34/13/i457/5045770},
	doi = {10.1093/bioinformatics/bty294},
	abstract = {AbstractMotivation.  The use of drug combinations, termed polypharmacy, is common to treat patients with complex diseases or co-existing conditions. However, a},
	language = {en},
	number = {13},
	urldate = {2019-09-10},
	journal = {Bioinformatics},
	author = {Zitnik, Marinka and Agrawal, Monica and Leskovec, Jure},
	month = jul,
	year = {2018},
	pages = {i457--i466}
}


@article{weintraub_escitalopram_2006,
	title = {Escitalopram for {Major} {Depression} in {Parkinson}’s {Disease}: {An} {Open}-{Label}, {Flexible}-{Dosage} {Study}},
	volume = {18},
	issn = {0895-0172},
	shorttitle = {Escitalopram for {Major} {Depression} in {Parkinson}’s {Disease}},
	url = {https://www.ncbi.nlm.nih.gov/pmc/articles/PMC1761053/},
	doi = {10.1176/appi.neuropsych.18.3.377},
	abstract = {Depression and antidepressant use are common in Parkinson’s disease, but the benefit of selective serotonin reuptake inhibitor (SSRI) treatment in this population has not been established. The authors treated 14 Parkinson’s disease patients with major depression with escitalopram in an open-label study. Although treatment was well tolerated and correlated with a significant decrease in Inventory of Depressive Symptomatology score, response and remission rates were only 21\% and 14\%, respectively. However, half of the subjects met Clinical Global Impression-Improvement criteria for response. In Parkinson’s disease, either SSRIs may have limited antidepressant effects, or the use of existing depression diagnostic and rating instruments may be problematic.},
	number = {3},
	urldate = {2019-09-12},
	journal = {The Journal of neuropsychiatry and clinical neurosciences},
	author = {Weintraub, Daniel and Taraborelli, Donna and Morales, Knashawn H. and Duda, John E. and Katz, Ira R. and Stern, Matthew B.},
	year = {2006},
	pmid = {16963587},
	pmcid = {PMC1761053},
	pages = {377--383}
}


@article{hauser_sertraline_1997,
	title = {Sertraline for the treatment of depression in {Parkinson}'s disease},
	volume = {12},
	issn = {0885-3185},
	doi = {10.1002/mds.870120522},
	abstract = {Although antidepressant medications are commonly used to treat depression in Parkinson's disease (PD), little information is available regarding their safety and efficacy in this condition. Sertraline is a relatively selective serotonin reuptake inhibitor with some dopamine reuptake inhibitor activity. It has a favorable tolerability profile, especially in the elderly. We undertook an open-label pilot evaluation of the safety and efficacy of sertraline to treat depression in PD. A total of 15 patients with PD and depression participated in the study. Sertraline was introduced at a daily dose of 25 mg for 1 week and then increased to 50 mg/day. Patients underwent evaluation at baseline and at a final visit approximately 7 weeks later. Sertraline was generally well tolerated, but five patients experienced side effects, and two discontinued medication. Patients taking selegiline experienced more adverse effects. Beck Depression Inventory scores improved significantly (mean +/- SE = 16.0 +/- 2.0 vs 11.7 +/- 1.9, p = 0.03), and Unified Parkinson's Disease Rating Scale and energy-level scores were unchanged. These results suggest that sertraline may be a useful treatment for depression in PD. As substantial placebo effects can occur in studies of PD and depression, placebo-controlled, double-blind studies are warranted.},
	language = {eng},
	number = {5},
	journal = {Movement Disorders: Official Journal of the Movement Disorder Society},
	author = {Hauser, R. A. and Zesiewicz, T. A.},
	month = sep,
	year = {1997},
	pmid = {9380061},
	keywords = {1-Naphthylamine, Aged, Antidepressive Agents, Second-Generation, Antiparkinson Agents, Depression, Drug Interactions, Drug Therapy, Combination, Humans, Parkinson Disease, Pilot Projects, Prospective Studies, Selegiline, Serotonin Uptake Inhibitors, Sertraline, Severity of Illness Index, Treatment Outcome},
	pages = {756--759}
}

@article{rampello_ssri_2002,
	title = {The {SSRI}, citalopram, improves bradykinesia in patients with {Parkinson}'s disease treated with {L}-dopa},
	volume = {25},
	issn = {0362-5664},
	abstract = {Idiopathic Parkinson's disease (IPD) is characterized by motor signs such as akinesia, rigidity, and often tremor at rest. In addition to these symptoms, depression is a common finding affecting 40\% of patients with IPD. This study evaluates the effect of the selective serotonin reuptake inhibitor, citalopram, on motor and nonmotor symptoms of depressed and nondepressed patients with IPD. Forty-six nondemented patients with IPD (24 men, 22 women; mean age 64 +/- 5.3 years; mean +/- SD disease duration, 6.4 +/- 3.2 years; mean +/- SD Hoehn-Yahr stage, 2.8 +/- 1.2) were included in the study. Patients were divided in two subgroups: depressed (n = 18) and nondepressed (n = 28). Citalopram was added in an unblinded manner, starting with 10 mg/d, and, after a week, increased up to 20 mg/d in the depressed subgroup (n = 18) and in half of the nondepressed subgroup (n = 14). Parkinsonian and depressive symptoms were evaluated before and after 1 and 4 months of treatment. Statistical evaluation was made by analysis of variance for repeated measures. Citalopram did not worsen motor performance in IPD, but improved bradykinesia and finger taps after 1 month and 4 months of treatment both in patients with and without depression (p {\textless} 0.05 versus baseline). A clear improvement in mood was also observed in 15 of 16 patients with depression. Although case reports indicate that citalopram can potentially worsen the motor symptoms in patients with PD, to date this effect has not been confirmed. Many of the symptoms, typically associated with depression, can be observed in nondepressed patients with IPD, because signs thought to represent depression can be produced by Parkinson's disease. In this study, we observed that when combined with levodopa, citalopram induces an improvement of motor performance, in particular of subscores 23 and 31 of Unified Parkinson's Disease Rating Scale both in depressed and in nondepressed patients with IPD.},
	language = {eng},
	number = {1},
	journal = {Clinical Neuropharmacology},
	author = {Rampello, Liborio and Chiechio, Santina and Raffaele, Rocco and Vecchio, Ignazio and Nicoletti, Francesco},
	month = feb,
	year = {2002},
	pmid = {11852292},
	keywords = {Affect, Antiparkinson Agents, Carbidopa, Citalopram, Depression, Dopamine Agents, Drug Combinations, Female, Humans, Hypokinesia, Levodopa, Male, Middle Aged, Parkinson Disease, Psychiatric Status Rating Scales, Psychomotor Performance, Serotonin Uptake Inhibitors},
	pages = {21--24}
}

@article{ceravolo_paroxetine_2000,
	title = {Paroxetine in {Parkinson}'s disease: effects on motor and depressive symptoms},
	volume = {55},
	issn = {0028-3878},
	shorttitle = {Paroxetine in {Parkinson}'s disease},
	doi = {10.1212/wnl.55.8.1216},
	abstract = {Selective serotonin reuptake inhibitors have been used in the treatment of depression in patients with PD. Conflicting data as to whether selective serotonin reuptake inhibitors worsen parkinsonian motor symptomatology have been reported. In this study, the additional 6 months therapy with paroxetine 20 mg/d in a group of depressed patients with PD did not modify parkinsonian motor function (Unified Parkinson's Disease Rating Scale scores); however, in one patient, fully reversible worsening of tremor was observed. Depression, as evaluated by Beck Depression Inventory and Hamilton Depression Rating Scale, improved from baseline to final visit (p {\textless} 0.05 by analysis of variance).},
	language = {eng},
	number = {8},
	journal = {Neurology},
	author = {Ceravolo, R. and Nuti, A. and Piccinni, A. and Dell'Agnello, G. and Bellini, G. and Gambaccini, G. and Dell'Osso, L. and Murri, L. and Bonuccelli, U.},
	month = oct,
	year = {2000},
	pmid = {11071504},
	keywords = {Aged, Analysis of Variance, Depressive Disorder, Female, Humans, Male, Middle Aged, Parkinson Disease, Paroxetine, Psychomotor Performance},
	pages = {1216--1218}
}


@article{menza_citalopram_2004,
	title = {Citalopram treatment of depression in {Parkinson}'s disease: the impact on anxiety, disability, and cognition},
	volume = {16},
	issn = {0895-0172},
	shorttitle = {Citalopram treatment of depression in {Parkinson}'s disease},
	doi = {10.1176/jnp.16.3.315},
	abstract = {Depression in Parkinson's disease (PD) is associated with faster progression of physical symptoms, greater decline in cognitive skills, and greater decline in the ability to care for oneself. The depression in these patients is also frequently comorbid with anxiety. There are no trials that provide data on the impact of depression treatment on anxiety, disability, and cognition in these patients. In this prospective, 8-week, open label trial, 10 patients with PD and major depression, without dementia, were given flexible doses of citalopram. Depression improved significantly and was associated with significant improvements in anxiety symptoms and functional impairment. The drug was well tolerated. This is the first study that provides data suggesting that treating depression in patients with PD may lead to improvements in anxiety and functional capacity. As with all nonrandomized, open-label trials at tertiary research centers, many nonspecific factors may have influenced the results.},
	language = {eng},
	number = {3},
	journal = {The Journal of Neuropsychiatry and Clinical Neurosciences},
	author = {Menza, Matthew and Marin, Humberto and Kaufman, Kenneth and Mark, Margery and Lauritano, Marc},
	year = {2004},
	pmid = {15377738},
	keywords = {Aged, Antidepressive Agents, Second-Generation, Anxiety, Citalopram, Cognition, Depression, Disability Evaluation, Female, Humans, Male, Neuropsychological Tests, Parkinson Disease, Prospective Studies, Severity of Illness Index},
	pages = {315--319}
}


@inproceedings{verma_efficacy_2012,
	title = {Efficacy and {Tolerability} of {Escitalopram} for {Treating} {Depression} in {Parkinson}’s {Disease}.},
	author = {Verma, Rohit and Anand, Kuljeet Singh},
	year = {2012},
	keywords = {Citalopram, Depressive disorder, Parkinson Disease, Parkinsonian Disorders}
}


@article{perez_lloret_pregabalin-induced_2009,
	title = {Pregabalin-induced parkinsonism: a case report},
	volume = {32},
	issn = {1537-162X},
	shorttitle = {Pregabalin-induced parkinsonism},
	doi = {10.1097/WNF.0b013e3181a9eb1b},
	abstract = {BACKGROUND: : Pregabalin (PGB) is an L-type, voltage-dependent, calcium-channel blocker, useful for the treatment of neuropathy pain and some forms of seizures. We report the case of a 64-year-old woman who developed full-blown parkinsonism after PGB administration.
CASE REPORT: : A female patient who developed diabetic sensory-motor polyneuropathy began treatment with gabapentin 300 mg plus amitriptyline 25 mg at the age of 56 years, with good tolerance. Eight years later, PGB 150 mg was added, after 3 months she developed a parkinsonian-like syndrome with axial symptoms, bilateral symmetric postural tremor, bradykinesia, and rigidity in both upper and lower limbs. Unified Parkinson's Disease Rating Scale motor score was 27/108 points at this time. Clear link to PGB introduction as well as symmetric limb involvement and symptom intensity made us suspect drug-induced parkinsonism. Pregabalin was withdrawn. Six months later, the patient had almost completely recovered; her Unified Parkinson's Disease Rating Scale motor score dropped to 4/108; and 3 months after that, it was 0.
CONCLUSION: : Although some cases of tremor were reported in clinical trials on PGB, to our knowledge, this is the first report of full-blown parkinsonism associated with PGB use. Vigilance of PGB-treated patients is recommended.},
	language = {eng},
	number = {6},
	journal = {Clinical Neuropharmacology},
	author = {Perez Lloret, Santiago and Amaya, Mariela and Merello, Marcelo},
	month = dec,
	year = {2009},
	pmid = {19952879},
	keywords = {Amines, Amitriptyline, Analgesics, Cyclohexanecarboxylic Acids, Diabetic Neuropathies, Drug Therapy, Combination, Female, Gabapentin, gamma-Aminobutyric Acid, Humans, Middle Aged, Parkinson Disease, Secondary, Pregabalin, Recovery of Function, Severity of Illness Index},
	pages = {353--354}
}

@article{younce_systematic_2019,
	title = {A {Systematic} {Review} and {Case} {Series} of {Ziprasidone} for {Psychosis} in {Parkinson}'s {Disease}},
	volume = {9},
	issn = {1877-718X},
	doi = {10.3233/JPD-181448},
	abstract = {The atypical antipsychotic ziprasidone has been considered inappropriate for use in patients with Parkinson's disease (PD), as most atypical antipsychotics worsen parkinsonism. However, the current evidence for safety and efficacy of ziprasidone in PDP has not been evaluated in a systematic fashion. We review published experience with ziprasidone for treating psychosis in PD via systematic search of MEDLINE, Embase, Cochrane CENTRAL, and Clinicaltrials.gov with terms related to "ziprasidone" and "Parkinson's disease", inclusive of case reports and prospective studies. We also add seven cases of ziprasidone exposure in patients in our center with idiopathic PD or Lewy body dementia (DLB), selected by retrospective query of all clinical data since 1996. In our review, two prospective trials and 11 case reports or series were found, with ziprasidone found to be generally effective for treatment of psychosis and with few adverse events reported. Our case series did not support efficacy of ziprasidone; it was generally safe in PD, but two patients with DLB had adverse motor events. We conclude that, although ziprasidone occasionally can produce substantial worsening of motor signs, it usually is well tolerated, and may provide in some cases a useful alternative to quetiapine, clozapine and pimavanserin, particularly in the acute care setting. Further randomized controlled studies are needed.},
	language = {eng},
	number = {1},
	journal = {Journal of Parkinson's Disease},
	author = {Younce, John R. and Davis, Albert A. and Black, Kevin J.},
	year = {2019},
	pmid = {30475775},
	pmcid = {PMC6398550},
	keywords = {Antipsychotic drugs, hallucinations, lewy body disease, parkinson’s disease, parkinsonism, psychotic disorders},
	pages = {63--71}
}

@article{ninan_case_2017,
	title = {A case report of olanzapine and related cardiac conduction changes},
	volume = {21},
	issn = {1931-227X},
	url = {https://onlinelibrary.wiley.com/doi/10.1002/pnp.480},
	doi = {10.1002/pnp.480},
	abstract = {The use of certain psychotropic medications can be associated with serious ventricular arrhythmia and sudden cardiac death. In this article, the authors use the QTc value in a patient treated with ol...},
	language = {en},
	number = {4},
	urldate = {2019-09-13},
	journal = {Progress in Neurology and Psychiatry},
	author = {Ninan, Sheeba and Hinchcliffe, David and Amo‐Korankye, Samuel},
	month = oct,
	year = {2017},
	pages = {13--16}
}

@article{puttegowda_olanzapine_2016,
	title = {Olanzapine {Induced} {Dilated} {Cardiomyopathy}},
	volume = {23},
	issn = {1394-195X},
	url = {https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4976704/},
	abstract = {A 28-year-old male patient with bipolar disorder taking olanzapine and lorazepam for almost 10 years presented with weight gain, diabetes, and anasarca was examined in this study. Evaluation of the patient revealed he was in heart failure. The reason for his heart failure was ambiguous and an investigation into it revealed negative results. Literature search conducted showed a few reported cases of putative olanzapine induced cardiomyopathy. One such relatively rare case is presented here.},
	number = {2},
	urldate = {2019-09-13},
	journal = {The Malaysian Journal of Medical Sciences : MJMS},
	author = {Puttegowda, Beeresha and Theodore, Joseph and Basappa, Ramesh and Nanjappa, Manjunath Cholenally},
	month = mar,
	year = {2016},
	pmid = {27547120},
	pmcid = {PMC4976704},
	pages = {82--84}
}


@article{cuesta_effects_2001,
	title = {Effects of olanzapine and other antipsychotics on cognitive function in chronic schizophrenia: a longitudinal study},
	volume = {48},
	issn = {0920-9964},
	shorttitle = {Effects of olanzapine and other antipsychotics on cognitive function in chronic schizophrenia},
	abstract = {This study aimed to determine the effect of olanzapine and other antipsychotic drugs on cognitive functions after 6months of treatment. Baseline, 3month and 6month psychopathological and cognitive evaluations were made. Thirty-eight partially responsive outpatients with DSM-IV chronic schizophrenia diagnosis were included in the study. On the indication of their attending psychiatrists, 21 patients initiated treatment with olanzapine, and 17 remained on their previous treatment with other antipsychotic drugs. Cognitive assessments were blind to medication and psychopathological status. The olanzapine group presented a significantly greater improvement in negative symptomatology and verbal memory than the comparison group in repeated-measures of MANOVAs between baseline, 3month and 6month assessments. These differences remained statistically significant after covarying out gender, treatment with other atypical antipsychotics, biperidene doses and changes in positive and negative symptoms. In order to match previous differences between groups, cognitive baseline scores for each test were introduced as covariates, resulting in a significant improvement for the olanzapine group in negative symptomatology and the interference task of the Stroop test.We then re-analyzed the data, dividing the comparison group into two groups: risperidone-treated patients (n=9) and patients receiving conventional antipsychotic drugs (n=8). Post-hoc analyses between groups were carried out with baseline cognitive assessment as covariate. The olanzapine group improved significantly more than the risperidone group in negative symptomatology and in the interference task of Stroop test. The improvement in the number of categories of the Wisconsin Card Sorting Test was higher in risperidone patients than in those receiving olanzapine or conventional antipsychotic treatment. Conventional antipsychotic drugs did not present a significant improvement over atypical antipsychotic drugs in any cognitive function. In summary, in patients suffering from chronic schizophrenia, atypical antipsychotic agents were associated with slight differential improvements over time in attentional, verbal memory and executive functions compared with conventional neuroleptic drugs. No differential improvements were found in social functioning, verbal fluency, non-verbal domains of memory or visuo-motor abilities.},
	language = {eng},
	number = {1},
	journal = {Schizophrenia Research},
	author = {Cuesta, M. J. and Peralta, V. and Zarzuela, A.},
	month = mar,
	year = {2001},
	pmid = {11278151},
	keywords = {Adult, Antipsychotic Agents, Benzodiazepines, Chronic Disease, Cognition, Female, Humans, Longitudinal Studies, Male, Multivariate Analysis, Olanzapine, Pirenzepine, Risperidone, Schizophrenia, Schizophrenic Psychology, Single-Blind Method},
	pages = {17--28}
}

@article{purdon_neuropsychological_2000,
	title = {Neuropsychological change in early phase schizophrenia during 12 months of treatment with olanzapine, risperidone, or haloperidol. {The} {Canadian} {Collaborative} {Group} for research in schizophrenia},
	volume = {57},
	issn = {0003-990X},
	doi = {10.1001/archpsyc.57.3.249},
	abstract = {BACKGROUND: The purpose of this investigation was to test the efficacy of novel antipsychotic medications in the treatment of cognitive impairment in early phase schizophrenia.
METHODS: Sixty-five patients in this multicenter double-blind study were randomly assigned to olanzapine (5-20 mg), risperidone (4-10 mg), or haloperidol (5-20 mg). Standard measures of clinical and motor syndromes were administered, as well as a comprehensive battery of tests to assess (1) motor skills, (2) attention span, (3) verbal fluency and reasoning, (4) nonverbal fluency and construction, (5) executive skills, and (6) immediate recall at baseline and after 6, 30, and 54 weeks of treatment.
RESULTS: The general cognitive index derived from the 6 domain scores revealed a significantly greater benefit from treatment with olanzapine relative to haloperidol and olanzapine relative to risperidone, but no significant difference was shown between risperidone and haloperidol. The improvement related to olanzapine was apparent after 6 weeks and enhanced after 30 and 54 weeks of treatment. Exploratory within-group analyses of the 6 cognitive domains after a conservative Bonferroni adjustment revealed a significant improvement with olanzapine only on the immediate recall domain, and similar analyses of the 17 individual tests revealed a significant improvement with olanzapine only on the Hooper Visual Organization Test.
CONCLUSIONS: These data suggest that olanzapine has some superior cognitive benefits relative to haloperidol and risperidone. A larger sample replication study is necessary to confirm and generalize the observations of this study and begin evaluation of the implications of this change to cerebral function and quality of life for people with schizophrenia.},
	language = {eng},
	number = {3},
	journal = {Archives of General Psychiatry},
	author = {Purdon, S. E. and Jones, B. D. and Stip, E. and Labelle, A. and Addington, D. and David, S. R. and Breier, A. and Tollefson, G. D.},
	month = mar,
	year = {2000},
	pmid = {10711911},
	keywords = {Adolescent, Adult, Aged, Antipsychotic Agents, Benzodiazepines, Cognition Disorders, Double-Blind Method, Female, Haloperidol, Humans, Male, Middle Aged, Motor Skills, Neuropsychological Tests, Olanzapine, Pirenzepine, Psychomotor Performance, Risperidone, Schizophrenia, Schizophrenic Psychology, Treatment Outcome},
	pages = {249--258}
}

@article{smith_effects_2001,
	title = {The effects of olanzapine on neurocognitive functioning in medication-refractory schizophrenia},
	volume = {4},
	issn = {1461-1457},
	doi = {10.1017/s146114570100253x},
	abstract = {Neurocognitive deficits are an enduring characteristic of schizophrenia, and remain prominent in patients whose positive symptoms have decreased after treatment with typical neuroleptics. Recent research has reported that olanzapine improves cognitive functioning in relapsing schizophrenia followed in an outpatient setting. Whether olanzapine will have an effect on improving cognitive function in chronic schizophrenics who have been hospitalized for long periods of time, and have shown a poor response to other conventional and atypical neuroleptics, has not been established. This study investigated cognitive function in chronic medication refractory schizophrenics who were treated with olanzapine or haloperidol in a double-blind study for 8 wk, and followed in an open olanzapine study for several additional months. Patients were evaluated with psychopathology rating scales and a battery of neuropsychological tests at baseline, end of double-blind and end of open-label phases of the study. At the end of the double-blind phase there were no significant differences between olanzapine and haloperidol, except for a trend for improvement on the Wisconsin Card Sort Test on olanzapine, which was significant at traditional but not corrected significance levels. After an additional 3 months of treatment with olanzapine doses of 20-40 mg/d, our statistical analysis showed significant improvement on overall neuropsychological test performance and specific cognitive tasks assessing verbal memory. However, these open-label results are difficult to interpret definitively because of the lack of a comparison drug group and the olanzapine dose escalation over time. Neurocognitive changes were not correlated with changes in psychopathology as assessed by PANSS or SANS scores.},
	language = {eng},
	number = {3},
	journal = {The International Journal of Neuropsychopharmacology},
	author = {Smith, R. C. and Infante, M. and Singh, A. and Khandat, A.},
	month = sep,
	year = {2001},
	pmid = {11669086},
	keywords = {Antipsychotic Agents, Benzodiazepines, Chronic Disease, Cognition, Double-Blind Method, Dyskinesia, Drug-Induced, Female, Haloperidol, Humans, Male, Memory, Middle Aged, Neuropsychological Tests, Olanzapine, Pirenzepine, Psychiatric Status Rating Scales, Schizophrenia, Schizophrenic Psychology},
	pages = {239--250}
}

@article{bilder_neurocognitive_2002,
	title = {Neurocognitive effects of clozapine, olanzapine, risperidone, and haloperidol in patients with chronic schizophrenia or schizoaffective disorder},
	volume = {159},
	issn = {0002-953X},
	doi = {10.1176/appi.ajp.159.6.1018},
	abstract = {OBJECTIVE: Newer antipsychotic drugs have shown promise in ameliorating neurocognitive deficits in patients with schizophrenia, but few studies have compared newer antipsychotic drugs with both clozapine and conventional agents, particularly in patients who have had suboptimal response to prior treatments.
METHOD: The authors examined the effects of clozapine, olanzapine, risperidone, and haloperidol on 16 measures of neurocognitive functioning in a double-blind, 14-week trial involving 101 patients. A global score was computed along with scores in four neurocognitive domains: memory, attention, motor function, and general executive and perceptual organization.
RESULTS: Global neurocognitive function improved with olanzapine and risperidone treatment, and these improvements were superior to those seen with haloperidol. Patients treated with olanzapine exhibited improvement in the general and attention domains but not more than that observed with other treatments. Patients treated with risperidone exhibited improvement in memory that was superior to that of both clozapine and haloperidol. Clozapine yielded improvement in motor function but not more than in other groups. Average effect sizes for change were in the small to medium range. More than half of the patients treated with olanzapine and risperidone experienced "clinically significant" improvement (changes in score of at least one-half standard deviation relative to baseline). These findings did not appear to be mediated by changes in symptoms, side effects, or blood levels of medications.
CONCLUSIONS: Patients with a history of suboptimal response to conventional treatments may show cognitive benefits from newer antipsychotic drugs, and there may be differences between atypical antipsychotic drugs in their patterns of cognitive effects.},
	language = {eng},
	number = {6},
	journal = {The American Journal of Psychiatry},
	author = {Bilder, Robert M. and Goldman, Robert S. and Volavka, Jan and Czobor, Pal and Hoptman, Matthew and Sheitman, Brian and Lindenmayer, Jean-Pierre and Citrome, Leslie and McEvoy, Joseph and Kunz, Michal and Chakos, Miranda and Cooper, Thomas B. and Horowitz, Terri L. and Lieberman, Jeffrey A.},
	month = jun,
	year = {2002},
	pmid = {12042192},
	keywords = {Adult, Antipsychotic Agents, Benzodiazepines, Clozapine, Cognition, Double-Blind Method, Female, Haloperidol, Humans, Male, Neuropsychological Tests, Olanzapine, Pirenzepine, Psychotic Disorders, Risperidone, Schizophrenia, Schizophrenic Psychology, Treatment Outcome},
	pages = {1018--1028}
}

@article{mcgurk_cognitive_2004,
	title = {Cognitive {Effects} of {Olanzapine} {Treatment} in {Schizophrenia}},
	volume = {6},
	issn = {1531-0132},
	url = {https://www.ncbi.nlm.nih.gov/pmc/articles/PMC1395781/},
	abstract = {Improvement in some but not all domains of cognition during treatment with the atypical antipsychotic drugs clozapine, quetiapine, olanzapine, and risperidone has been reported in some but not all studies. It has been recently suggested that these reports are an artifact, related to lessening of the impairment due to typical neuroleptic drugs and anticholinergic agents. The purpose of this study was to further test the hypothesis that olanzapine, an atypical antipsychotic drug reported to have anticholinergic properties, improves cognition in patients with schizophrenia, including domains of cognition related closely to work and social function (ie, verbal learning and memory) and that this improvement is independent of improvement in psychopathology.


Thirty-four patients with schizophrenia who were partial responders to typical antipsychotic drug treatment were evaluated with a comprehensive neurocognitive battery, including measures of executive functioning; verbal and visual learning and memory; working memory; immediate, selective, and sustained attention; perceptual/motor processing; and motor skills prior to and following treatment with olanzapine for 6 weeks. The Brief Psychiatric Rating Scale (BPRS) was used to assess psychopathology in patients treated with typical antipsychotic drugs. Subjects were switched to olanzapine (average dose 13.4 mg, range 5–20 mg) and reassessed following 6 weeks and 6 months of treatment. Significant improvement was noted in 9 of 19 cognitive tests, including measures of selective attention, verbal learning and memory, and verbal fluency. No cognitive test was worsened by olanzapine treatment. Improvements in the BPRS Total and Positive Symptom Subscale scores were noted. Improvements in verbal learning and memory, sustained attention, and psychomotor tracking were independent of improvement in psychopathology. These data suggest that olanzapine improved some but not all cognitive deficits in schizophrenia, including verbal memory, a cognitive domain impaired by anticholinergic drugs. The basis for the improvement in cognitive scores, which should lead to improvement in role functioning if real, is discussed.},
	number = {2},
	urldate = {2019-09-13},
	journal = {Medscape General Medicine},
	author = {McGurk, Susan R and Lee, M.A and Jayathilake, K and Meltzer, Herbert Y},
	month = may,
	year = {2004},
	pmid = {15266253},
	pmcid = {PMC1395781}
}


@article{noauthor_antipsychotic_nodate,
	title = {Antipsychotic {Drugs} and {Liver} {Injury}},
	volume = {30},
	issn = {1002-0829},
	url = {https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5925599/},
	doi = {10.11919/j.issn.1002-0829.217090},
	abstract = {In October 2015, the drug-induced liver diseases group of the Chinese Society of Hepatology drafted and published the first Diagnosis and Treatment Guidelines on Drug-induced Liver Injury in China, giving suggestions on the diagnosis and treatment of drug-induced liver injury (DILI). As a psychiatrist, I have found that in clinical practice both typical and new antipsychotic drugs can induce liver injury to varying degrees. Therefore, it is necessary to quickly and accurately determine the cause of liver injury and the type and severity of injury and establish a solution. This article reviewed relevant literature including the common pathogenesis and clinical manifestations of drug-induced liver injury caused by antipsychotic drugs, laboratory tests, diagnostic criteria and classification, and clinical management strategies. This paper also includes a summary and a perspective on liver injury caused by antipsychotic drugs.},
	number = {1},
	urldate = {2019-09-13},
	journal = {Shanghai Archives of Psychiatry},
	pmid = {29719358},
	pmcid = {PMC5925599},
	pages = {47--51}
}


@article{lv_antipsychotic_2018,
	title = {Antipsychotic {Drugs} and {Liver} {Injury}},
	volume = {30},
	issn = {1002-0829},
	doi = {10.11919/j.issn.1002-0829.217090},
	abstract = {In October 2015, the drug-induced liver diseases group of the Chinese Society of Hepatology drafted and published the first Diagnosis and Treatment Guidelines on Drug-induced Liver Injury in China, giving suggestions on the diagnosis and treatment of drug-induced liver injury (DILI). As a psychiatrist, I have found that in clinical practice both typical and new antipsychotic drugs can induce liver injury to varying degrees. Therefore, it is necessary to quickly and accurately determine the cause of liver injury and the type and severity of injury and establish a solution. This article reviewed relevant literature including the common pathogenesis and clinical manifestations of drug-induced liver injury caused by antipsychotic drugs, laboratory tests, diagnostic criteria and classification, and clinical management strategies. This paper also includes a summary and a perspective on liver injury caused by antipsychotic drugs.},
	language = {eng},
	number = {1},
	journal = {Shanghai Archives of Psychiatry},
	author = {Lv, Qinyu and Yi, Zhenghui},
	month = feb,
	year = {2018},
	pmid = {29719358},
	pmcid = {PMC5925599},
	keywords = {antipsychotic drugs, drug-induced liver injury},
	pages = {47--51}
}


@article{boiko_pharmacogenetics_2019,
	title = {Pharmacogenetics of tardive dyskinesia in schizophrenia: {The} role of {CHRM}1 and {CHRM}2 muscarinic receptors},
	issn = {1814-1412},
	shorttitle = {Pharmacogenetics of tardive dyskinesia in schizophrenia},
	doi = {10.1080/15622975.2018.1548780},
	abstract = {OBJECTIVES: Acetylcholine M (muscarinic) receptors are possibly involved in tardive dyskinesia (TD). The authors tried to verify this hypothesis by testing for possible associations between two muscarinic receptor genes (CHRM1 and CHRM2) polymorphisms and TD in patients with schizophrenia.
METHODS: A total of 472 patients with schizophrenia were recruited. TD was assessed cross-sectionally using the Abnormal Involuntary Movement Scale. Fourteen allelic variants of CHRM1 and CHRM2 were genotyped using Applied Biosystems amplifiers (USA) and the MassARRAY System by Agena Bioscience.
RESULTS: The prevalence of the rs1824024*GG genotype of the CHRM2 gene was lower in TD patients compared to the group without it (χ2 = 6.035, p = 0.049). This suggested that this genotype has a protective effect for the development of TD (OR = 0.4, 95\% CI: 0.19-0.88). When age, gender, duration of schizophrenia and dosage of antipsychotic treatment were added as covariates in regression analysis, the results did not reach statistical significance.
CONCLUSIONS: This study did identify associations between CHRM2 variations and TD; the results of logistic regression analysis with covariates suggest that the association is, however, likely to be secondary to other concomitant factors.},
	language = {eng},
	journal = {The World Journal of Biological Psychiatry: The Official Journal of the World Federation of Societies of Biological Psychiatry},
	author = {Boiko, Anastasiia S. and Ivanova, Svetlana A. and Pozhidaev, Ivan V. and Freidin, Maxim B. and Osmanova, Diana Z. and Fedorenko, Olga Yu and Semke, Arkadyi V. and Bokhan, Nikolay A. and Wilffert, Bob and Loonen, Anton J. M.},
	month = jan,
	year = {2019},
	pmid = {30623717},
	keywords = {muscarinic acetylcholine receptors, pharmacogenetics, polymorphisms, schizophrenia, Tardive dyskinesia},
	pages = {1--6}
}


@article{dean_possible_2015,
	title = {Possible involvement of muscarinic receptors in psychiatric disorders: a focus on schizophrenia and mood disorders.},
	volume = {15},
	issn = {1566-5240},
	shorttitle = {Possible involvement of muscarinic receptors in psychiatric disorders},
	url = {http://europepmc.org/abstract/MED/25817858},
	doi = {10.2174/1566524015666150330144821},
	abstract = {Abstract: A considerable body of data supports a role for the central cholinergic system in the aetiologies of schizophrenia and mood disorders. There have...},
	language = {eng},
	number = {3},
	urldate = {2019-09-13},
	journal = {Current molecular medicine},
	author = {Dean, B. and Scarr, E.},
	year = {2015},
	pmid = {25817858},
	pages = {253--264}
}

@article{jeon_role_2015,
	title = {The {Role} of {Muscarinic} {Receptors} in the {Pathophysiology} of {Mood} {Disorders}: {A} {Potential} {Novel} {Treatment}?},
	volume = {13},
	issn = {1570-159X},
	shorttitle = {The {Role} of {Muscarinic} {Receptors} in the {Pathophysiology} of {Mood} {Disorders}},
	url = {https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4759313/},
	doi = {10.2174/1570159X13666150612230045},
	abstract = {The central cholinergic system has been implicated in the pathophysiology of mood disorders. An imbalance in central cholinergic neurotransmitter activity has been proposed to contribute to the manic and depressive episodes typical of these disorders. Neuropharmacological studies into the effects of cholinergic agonists and antagonists on mood state have provided considerable support for this hypothesis. Furthermore, recent clinical studies have shown that the pan-CHRM antagonist, scopolamine, produces rapid-acting antidepressant effects in individuals with either major depressive disorder (MDD) or bipolar disorder (BPD), such as bipolar depression, contrasting the delayed therapeutic response of conventional mood stabilisers and antidepressants. This review presents recent data from neuroimaging, post-mortem and genetic studies supporting the involvement of muscarinic cholinergic receptors (CHRMs), particularly CHRM2, in the pathophysiology of MDD and BPD. Thus, novel drugs that selectively target CHRMs with negligible effects in the peripheral nervous system might produce more rapid and robust clinical improvement in patients with BPD and MDD.},
	number = {6},
	urldate = {2019-09-13},
	journal = {Current Neuropharmacology},
	author = {Jeon, Won Je and Dean, Brian and Scarr, Elizabeth and Gibbons, Andrew},
	month = dec,
	year = {2015},
	pmid = {26630954},
	pmcid = {PMC4759313},
	pages = {739--749}
}

@article{drevets_antidepressant_2013,
	title = {Antidepressant {Effects} of the {Muscarinic} {Cholinergic} {Receptor} {Antagonist} {Scopolamine}: {A} {Review}},
	volume = {73},
	issn = {0006-3223},
	shorttitle = {Antidepressant {Effects} of the {Muscarinic} {Cholinergic} {Receptor} {Antagonist} {Scopolamine}},
	url = {https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4131859/},
	doi = {10.1016/j.biopsych.2012.09.031},
	abstract = {The muscarinic cholinergic receptor system has been implicated in the pathophysiology of depression, with physiological evidence indicating this system is overactive or hyperresponsive in depression and with genetic evidence showing that variation in genes coding for receptors within this system are associated with higher risk for depression. In studies aimed at assessing whether a reduction in muscarinic cholinergic receptor function would improve depressive symptoms, the muscarinic receptor antagonist scopolamine manifested antidepressant effects that were robust and rapid relative to conventional pharmacotherapies. Here, we review the data from a series of randomized, double-blind, placebo-controlled studies involving subjects with unipolar or bipolar depression treated with parenteral doses of scopolamine. The onset and duration of the antidepressant response are considered in light of scopolamine's pharmacokinetic properties and an emerging literature that characterizes scopolamine's effects on neurobiological systems beyond the cholinergic system that appear relevant to the neurobiology of mood disorders. Scopolamine infused at 4.0 μg/kg intravenously produced robust antidepressant effects versus placebo, which were evident within 3 days after the initial infusion. Placebo-adjusted remission rates were 56\% and 45\% for the initial and subsequent replication studies, respectively. While effective in male and female subjects, the change in depression ratings was greater in female subjects. Clinical improvement persisted more than 2 weeks following the final infusion. The timing and persistence of the antidepressant response to scopolamine suggest a mechanism beyond that of direct muscarinic cholinergic antagonism. These temporal relationships suggest that scopolamine-induced changes in gene expression and synaptic plasticity may confer the therapeutic mechanism.},
	number = {12},
	urldate = {2019-09-13},
	journal = {Biological psychiatry},
	author = {Drevets, Wayne C. and Zarate, Carlos A. and Furey, Maura L.},
	month = jun,
	year = {2013},
	pmid = {23200525},
	pmcid = {PMC4131859},
	pages = {1156--1163}
}


@incollection{carlson_physiology_2019,
	address = {Treasure Island (FL)},
	title = {Physiology, {Cholinergic} {Receptors}},
	copyright = {Copyright © 2019, StatPearls Publishing LLC.},
	url = {http://www.ncbi.nlm.nih.gov/books/NBK526134/},
	abstract = {Cholinergic receptors function in signal transduction of the somatic and autonomic nervous system. The receptors are named because they are activated by the ligand acetylcholine. These receptors are subdivided into nicotinic and muscarinic receptors which are named secondary to separate activating ligands that contributed to their study. Nicotinic receptors are responsive to the agonist nicotine, while muscarinic receptors are responsive to muscarine. The two receptors differ in function as ionotropic ligand-gated and G-protein coupled receptors, respectively. Nicotinic receptors function within the central nervous system and at the neuromuscular junction. While muscarinic receptors function in both the peripheral and central nervous system, mediating innervation to visceral organs. The difference in signal transduction of the two receptor types confers separate physiological function upon receptor activation. Furthermore, differences in receptor subtypes create unique implications for pharmacologic targets and pathogenesis of the disease.},
	language = {eng},
	urldate = {2019-09-13},
	booktitle = {{StatPearls}},
	publisher = {StatPearls Publishing},
	author = {Carlson, Adlei B. and Kraus, Gregory P.},
	year = {2019},
	pmid = {30252390}
}

@article{broido_scale-free_2019,
	title = {Scale-free networks are rare},
	volume = {10},
	copyright = {2019 The Author(s)},
	issn = {2041-1723},
	url = {https://www.nature.com/articles/s41467-019-08746-5},
	doi = {10.1038/s41467-019-08746-5},
	abstract = {Real-world networks are often said to be ”scale free”, meaning their degree distribution follows a power law. Broido and Clauset perform statistical tests of this claim using a large and diverse corpus of real-world networks, showing that scale-free structure is far from universal.},
	language = {en},
	number = {1},
	urldate = {2019-09-14},
	journal = {Nature Communications},
	author = {Broido, Anna D. and Clauset, Aaron},
	month = mar,
	year = {2019},
	pages = {1--10}
}

@inproceedings{newman_random_2002,
	title = {Random {Graphs} as {Models} of {Networks}},
	doi = {10.1002/3527602755.ch2},
	abstract = {The random graph of Erdos and Renyi is one of the oldest and best studied models of a network, and possesses the considerable advantage of being exactly solvable for many of its average properties. However, as a model of real-world networks such as the Internet, social networks or biological networks it leaves a lot to be desired. In particular, it differs from real networks in two crucial ways: it lacks network clustering or transitivity, and it has an unrealistic Poissonian degree distribution. In this paper we review some recent work on generalizations of the random graph aimed at correcting these shortcomings. We describe generalized random graph models of both directed and undirected networks that incorporate arbitrary non-Poisson degree distributions, and extensions of these models that incorporate clustering too. We also describe two recent applications of random graph models to the problems of network robustness and of epidemics spreading on contact networks.},
	author = {Newman, Mark E. J.},
	year = {2002},
	keywords = {Biological network, Cluster analysis, Cortical Spreading Depression, Decision problem, Degree distribution, Graph - visual representation, Graph (discrete mathematics), Internet, Random graph, Robustness of complex networks, Social network, statistical cluster, Vertex-transitive graph}
}


@article{flank_olanzapine_2018,
	title = {Olanzapine for prevention of chemotherapy-induced nausea and vomiting in children and adolescents: a multi-center, feasibility study},
	volume = {26},
	issn = {1433-7339},
	shorttitle = {Olanzapine for prevention of chemotherapy-induced nausea and vomiting in children and adolescents},
	doi = {10.1007/s00520-017-3864-8},
	abstract = {CONTEXT: There are no prospective pediatric trials evaluating olanzapine for chemotherapy-induced nausea and vomiting (CINV) prevention.
OBJECTIVE: This study evaluated the feasibility of a trial of olanzapine to evaluate the contribution of olanzapine to CINV control in pediatric oncology patients.
METHODS: Patients {\textless} 18 years receiving CINV prophylaxis with ondansetron/granisetron/palonosetron ± dexamethasone ± aprepitant were eligible to participate in this prospective, single-arm, open-label study. All patients received olanzapine (0.14 mg/kg/dose; max 10 mg/dose) once daily orally starting before the first chemotherapy dose and continuing for up to four doses after the last chemotherapy administration. A future trial was considered feasible if mean time to enroll 15 patients was ≤ 12 months/site, ≥ 12/15 took at least half of the planned olanzapine doses, and ≤ 3/15 experienced significant sedation or dizziness despite dose reduction. The proportion of children who experienced complete CINV control (no nausea, vomiting, or retching) was described.
RESULTS: Fifteen patients (range 4.1-17.4 years) participated; mean recruitment period was 9.3 months/site. All patients took at least half of the planned olanzapine doses. Six patients experienced sedation which resolved with olanzapine dose reduction (N = 5) or bedtime administration (N = 1). Olanzapine was stopped in one patient with blurry vision and in another with increased plasma GGT values. In both the acute and delayed phases, eight patients experienced complete control of vomiting but almost all (14/15) had nausea.
CONCLUSION: A pediatric trial of olanzapine for CINV control is feasible. Our findings will inform the design of a future study.},
	language = {eng},
	number = {2},
	journal = {Supportive Care in Cancer: Official Journal of the Multinational Association of Supportive Care in Cancer},
	author = {Flank, J. and Schechter, T. and Gibson, P. and Johnston, D. L. and Orsey, A. D. and Portwine, C. and Sung, L. and Dupuis, L. L.},
	year = {2018},
	pmid = {28856448},
	keywords = {Adolescent, Antiemetics, Chemotherapy-induced nausea, Chemotherapy-induced vomiting, Child, Child, Preschool, Feasibility Studies, Female, Humans, Male, Nausea, Olanzapine, Pediatrics, Vomiting},
	pages = {549--555}
}

@article{himmerich_olanzapine_2017,
	title = {Olanzapine {Treatment} for {Patients} with {Anorexia} {Nervosa}},
	volume = {62},
	issn = {1497-0015},
	doi = {10.1177/0706743717709967},
	language = {eng},
	number = {7},
	journal = {Canadian Journal of Psychiatry. Revue Canadienne De Psychiatrie},
	author = {Himmerich, Hubertus and Au, Katie and Dornik, Julia and Bentley, Jessica and Schmidt, Ulrike and Treasure, Janet},
	year = {2017},
	pmid = {28683226},
	pmcid = {PMC5528990},
	keywords = {anorexia nervosa, Anorexia Nervosa, Antipsychotic Agents, antipsychotics, Benzodiazepines, Humans, olanzapine, Olanzapine},
	pages = {506--507}
}