manual-references.json

[
 {
  "id": "url:http://www.bioinf.at/publications/2014/NIPS2014a.pdf",
  "type": "article-journal",
  "title": "Deep learning as an opportunity in virtual screening",
  "container-title": "Neural Information Processing Systems 2014: Deep Learning and Representation Learning Workshop",
  "URL": "http://www.dlworkshop.org/23.pdf?attredirects=0",
  "author": [
   {
    "family": "Unterthiner",
    "given": "Thomas"
   },
   {
    "family": "Mayr",
    "given": "Andreas"
   },
   {
    "family": "Klambauer",
    "given": "Günter"
   },
   {
    "family": "Steijaert",
    "given": "Marvin"
   },
   {
    "family": "Wegner",
    "given": "Jörg K."
   },
   {
    "family": "Ceulemans",
    "given": "Hugo"
   },
   {
    "family": "Hochreiter",
    "given": "Sepp"
   }
  ],
  "issued": {
   "date-parts": [
    [
     "2014"
    ]
   ]
  },
  "accessed": {
   "date-parts": [
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     "2017",
     5,
     19
    ]
   ]
  }
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  "id": "url:https://openreview.net/pdf?id=rJWF0Fywf",
  "type": "article-journal",
  "title": "Winner's Curse? On Pace, Progress, and Empirical Rigor ...",
  "container-title": "International Conference on Learning Representations 2018",
  "URL": "https://openreview.net/pdf?id=rJWF0Fywf",
  "author": [
   {
    "family": "Sculley",
    "given": "D."
   },
   {
    "family": "Snoek",
    "given": "Jasper"
   },
   {
    "family": "Rahimi",
    "given": "Ali"
   },
   {
    "family": "Wiltschko",
    "given": "Alex"
   }
  ],
  "issued": {
   "date-parts": [
    [
     "2018"
    ]
   ]
  },
  "accessed": {
   "date-parts": [
    [
     "2020",
     2,
     14
    ]
   ]
  }
 },
 {
  "id": "url:https://repozitorij.uni-lj.si/IzpisGradiva.php?id=85515",
  "type": "report",
  "title": "Globoko ucenje na genomskih in filogenetskih podatkih",
  "publisher": "Univerza v Ljubljani, Fakulteta za racunalništvo in informatiko",
  "source": "repozitorij.uni-lj.si",
  "URL": "https://repozitorij.uni-lj.si/IzpisGradiva.php?id=85515",
  "language": "slv",
  "author": [
   {
    "family": "Mrzelj",
    "given": "Nina"
   }
  ],
  "issued": {
   "date-parts": [
    [
     "2016"
    ]
   ]
  },
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     "2017",
     5,
     23
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   ]
  }
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  "id": "doi:10.1093/bib/bbw110",
  "type": "article-journal",
  "title": "A review of validation strategies for computational drug repositioning",
  "container-title": "Briefings in Bioinformatics",
  "source": "academic.oup.com",
  "URL": "https://academic.oup.com/bib/article/doi/10.1093/bib/bbw110/2562646/A-review-of-validation-strategies-for",
  "DOI": "10.1093/bib/bbw110",
  "journalAbbreviation": "Brief Bioinform",
  "author": [
   {
    "family": "Brown",
    "given": "Adam S."
   },
   {
    "family": "Patel",
    "given": "Chirag J."
   }
  ],
  "issued": {
   "date-parts": [
    [
     "2016",
     11,
     22
    ]
   ]
  },
  "accessed": {
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     "2017",
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 },
 {
  "id": "url:http://www.iro.umontreal.ca/~lisa/publications2/index.php/publications/show/247",
  "type": "report",
  "title": "Visualizing Higher-Layer Features of a Deep Network",
  "publisher": "University of Montreal",
  "abstract": "Deep architectures have demonstrated state-of-the-art results in a variety of settings, especially with vision datasets. Beyond the model definitions and the quantitative analyses, there is a need for qualitative comparisons of the solutions learned by various deep architectures. The goal of this paper is to find good qualitative interpretations of high level features represented by such models. To this end, we contrast and compare several techniques applied on Stacked Denoising Autoencoders and Deep Belief Networks, trained on several vision datasets. We show that, perhaps counter-intuitively, such interpretation is possible at the unit level, that it is simple to accomplish and that the results are consistent across various techniques. We hope that such techniques will allow researchers in deep architectures to understand more of how and why deep architectures work",
  "URL": "http://www.iro.umontreal.ca/~lisa/publications2/index.php/publications/show/247",
  "number": "1341",
  "author": [
   {
    "family": "Erhan",
    "given": "Dumitru"
   },
   {
    "family": "Bengio",
    "given": "Yoshua"
   },
   {
    "family": "Courville",
    "given": "Aaron"
   },
   {
    "family": "Vincent",
    "given": "Pascal"
   }
  ],
  "issued": {
   "date-parts": [
    [
     "2009",
     6
    ]
   ]
  }
 },
 {
  "id": "url:https://openreview.net/pdf?id=Sk-oDY9ge",
  "type": "article-journal",
  "title": "Diet Networks: Thin Parameters for Fat Genomics",
  "container-title": "International Conference on Learning Representations 2017",
  "source": "openreview.net",
  "URL": "https://openreview.net/forum?id=Sk-oDY9ge&noteId=Sk-oDY9ge",
  "shortTitle": "Diet Networks",
  "journalAbbreviation": "ICLR 2017",
  "author": [
   {
    "family": "Romero",
    "given": "Adriana"
   },
   {
    "family": "Carrier",
    "given": "Pierre Luc"
   },
   {
    "family": "Erraqabi",
    "given": "Akram"
   },
   {
    "family": "Sylvain",
    "given": "Tristan"
   },
   {
    "family": "Auvolat",
    "given": "Alex"
   },
   {
    "family": "Dejoie",
    "given": "Etienne"
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   {
    "family": "Legault",
    "given": "Marc-André"
   },
   {
    "family": "Dubé",
    "given": "Marie-Pierre"
   },
   {
    "family": "Hussin",
    "given": "Julie G."
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   {
    "family": "Bengio",
    "given": "Yoshua"
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     "2016",
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     4
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  "id": "url:https://papers.nips.cc/paper/4443-algorithms-for-hyper-parameter-optimization.pdf",
  "type": "paper-conference",
  "title": "Algorithms for Hyper-parameter Optimization",
  "container-title": "Proceedings of the 24th International Conference on Neural Information Processing Systems",
  "collection-title": "NIPS'11",
  "publisher": "Curran Associates Inc.",
  "publisher-place": "USA",
  "page": "2546–2554",
  "source": "ACM Digital Library",
  "event-place": "USA",
  "abstract": "Several recent advances to the state of the art in image classification benchmarks have come from better configurations of existing techniques rather than novel approaches to feature learning. Traditionally, hyper-parameter optimization has been the job of humans because they can be very efficient in regimes where only a few trials are possible. Presently, computer clusters and GPU processors make it possible to run more trials and we show that algorithmic approaches can find better results. We present hyper-parameter optimization results on tasks of training neural networks and deep belief networks (DBNs). We optimize hyper-parameters using random search and two new greedy sequential methods based on the expected improvement criterion. Random search has been shown to be sufficiently efficient for learning neural networks for several datasets, but we show it is unreliable for training DBNs. The sequential algorithms are applied to the most difficult DBN learning problems from [1] and find significantly better results than the best previously reported. This work contributes novel techniques for making response surface models P(y|x) in which many elements of hyper-parameter assignment (x) are known to be irrelevant given particular values of other elements.",
  "URL": "http://dl.acm.org/citation.cfm?id=2986459.2986743",
  "ISBN": "978-1-61839-599-3",
  "author": [
   {
    "family": "Bergstra",
    "given": "James"
   },
   {
    "family": "Bardenet",
    "given": "Rémi"
   },
   {
    "family": "Bengio",
    "given": "Yoshua"
   },
   {
    "family": "Kégl",
    "given": "Balázs"
   }
  ],
  "issued": {
   "date-parts": [
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     "2011"
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     "2017",
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  "id": "url:https://ai.stanford.edu/~ang/papers/icml11-MultimodalDeepLearning.pdf",
  "type": "paper-conference",
  "title": "Multimodal Deep Learning",
  "container-title": "Proceedings of the 28th International Conference on Machine Learning",
  "source": "Google Scholar",
  "URL": "https://ccrma.stanford.edu/~juhan/pubs/NgiamKhoslaKimNamLeeNg2011.pdf",
  "author": [
   {
    "family": "Ngiam",
    "given": "Jiquan"
   },
   {
    "family": "Khosla",
    "given": "Aditya"
   },
   {
    "family": "Kim",
    "given": "Mingyu"
   },
   {
    "family": "Nam",
    "given": "Juhan"
   },
   {
    "family": "Lee",
    "given": "Honglak"
   },
   {
    "family": "Ng",
    "given": "Andrew Y."
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     "2011"
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  "id": "url:https://papers.nips.cc/paper/4824-imagenet-classification-with-deep-convolutional-neural-networks.pdf",
  "type": "paper-conference",
  "title": "ImageNet Classification with Deep Convolutional Neural Networks",
  "container-title": "Proceedings of the 25th International Conference on Neural Information Processing Systems",
  "collection-title": "NIPS'12",
  "publisher": "Curran Associates Inc.",
  "publisher-place": "USA",
  "page": "1097–1105",
  "source": "ACM Digital Library",
  "event-place": "USA",
  "abstract": "We trained a large, deep convolutional neural network to classify the 1.2 million high-resolution images in the ImageNet LSVRC-2010 contest into the 1000 different classes. On the test data, we achieved top-1 and top-5 error rates of 37.5% and 17.0% which is considerably better than the previous state-of-the-art. The neural network, which has 60 million parameters and 650,000 neurons, consists of five convolutional layers, some of which are followed by max-pooling layers, and three fully-connected layers with a final 1000-way softmax. To make training faster, we used non-saturating neurons and a very efficient GPU implementation of the convolution operation. To reduce overriding in the fully-connected layers we employed a recently-developed regularization method called \"dropout\" that proved to be very effective. We also entered a variant of this model in the ILSVRC-2012 competition and achieved a winning top-5 test error rate of 15.3%, compared to 26.2% achieved by the second-best entry.",
  "URL": "http://dl.acm.org/citation.cfm?id=2999134.2999257",
  "author": [
   {
    "family": "Krizhevsky",
    "given": "Alex"
   },
   {
    "family": "Sutskever",
    "given": "Ilya"
   },
   {
    "family": "Hinton",
    "given": "Geoffrey E."
   }
  ],
  "issued": {
   "date-parts": [
    [
     "2012"
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  "id": "url:https://eprint.iacr.org/2017/281.pdf",
  "type": "report",
  "title": "Practical Secure Aggregation for Privacy Preserving Machine Learning",
  "genre": "Cryptology ePrint Archive",
  "source": "ePrint IACR",
  "abstract": "We design a novel, communication-efficient, failure-robust protocol for secure aggregation of high-dimensional data. Our protocol allows a server to compute the sum of large, user-held data vectors from mobile devices in a secure manner (i.e. without learning each user's individual contribution), and can be used, for example, in a federated learning setting, to aggregate user-provided model updates for a deep neural network. We prove the security of our protocol in the honest-but-curious and malicious settings, and show that security is maintained even if an arbitrarily chosen subset of users drop out at any time. We evaluate the efficiency of our protocol and show, by complexity analysis and a concrete implementation, that its runtime and communication overhead remain low even on large data sets and client pools. For 16-bit input values, our protocol offerscommunication expansion forusers and-dimensional vectors, andexpansion for\n\nusers and-dimensional vectors over sending data in the clear.",
  "URL": "https://eprint.iacr.org/2017/281",
  "number": "281",
  "author": [
   {
    "family": "Bonawitz",
    "given": "Keith"
   },
   {
    "family": "Ivanov",
    "given": "Vladimir"
   },
   {
    "family": "Kreuter",
    "given": "Ben"
   },
   {
    "family": "Marcedone",
    "given": "Antonio"
   },
   {
    "family": "McMahan",
    "given": "H. Brendan"
   },
   {
    "family": "Patel",
    "given": "Sarvar"
   },
   {
    "family": "Ramage",
    "given": "Daniel"
   },
   {
    "family": "Segal",
    "given": "Aaron"
   },
   {
    "family": "Seth",
    "given": "Karn"
   }
  ],
  "issued": {
   "date-parts": [
    [
     "2017"
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     "2017",
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  {
    "URL": "http://www.intel.com/pressroom/archive/speeches/ag080998.htm",
    "title": "Academy of Management: Andrew S. Grove",
    "issued": {
      "date-parts": [
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          1998,
          8,
          9
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    "author": [
      {
        "family": "Grove",
        "given": "Andrew S."
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    "type": "webpage",
    "id": "url:http://www.intel.com/pressroom/archive/speeches/ag080998.htm"
  },
  {
    "URL": "https://www.nigms.nih.gov/Education/Documents/curiosity.pdf",
    "title": "Curiosity Creates Cures: The Value and Impact of Basic Research",
    "issued": {
      "date-parts": [
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          2012,
          5
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    },
    "author": [
      {
        "family": "NIH"
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    "id": "url:https://www.nigms.nih.gov/Education/Documents/curiosity.pdf"
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  {
    "URL": "http://www.predictioncenter.org/casp12/rrc_avrg_results.cgi",
    "title": "RR Results - CASP12",
    "issued": {
      "date-parts": [
        [
          2017
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      ]
    },
    "type": "webpage",
    "id": "url:http://www.predictioncenter.org/casp12/rrc_avrg_results.cgi"
  },
  {
    "URL": "https://www.cameo3d.org/",
    "title": "CAMEO - Continuous Automated Model Evaluation",
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      "date-parts": [
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          2017
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      ]
    },
    "redirects-to": "https://www.cameo3d.org/",
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    "id": "url:https://www.cameo3d.org"
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    "URL": "https://github.com/deepchem/deepchem",
    "title": "deepchem/deepchem",
    "container-title": "GitHub",
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          2017
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    "type": "webpage",
    "id": "url:https://github.com/deepchem/deepchem"
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    "URL": "http://googleresearch.blogspot.co.uk/2015/06/inceptionism-going-deeper-into-neural.html",
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    "issued": {
      "date-parts": [
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        "given": "Alexander",
        "family": "Mordvintsev"
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        "given": "Christopher",
        "family": "Olah"
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        "given": "Mike",
        "family": "Tyka"
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    "URL": "https://github.com/fchollet/keras",
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          2017
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    "id": "url:https://github.com/fchollet/keras"
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    "URL": "https://github.com/maxpumperla/elephas",
    "title": "maxpumperla/elephas",
    "container-title": "GitHub",
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          2017
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    "id": "url:https://github.com/maxpumperla/elephas"
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  {
    "URL": "https://www.synapse.org/#!Synapse:syn6131484/wiki/402026",
    "title": "ENCODE-DREAM in vivo Transcription Factor Binding Site Prediction Challenge",
    "issued": {
      "date-parts": [
        [
          2017
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      ]
    },
    "type": "webpage",
    "id": "url:https://www.synapse.org/#!Synapse:syn6131484/wiki/402026"
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  {
    "URL": "http://www.businessinsider.com/ibm-edges-closer-to-human-speech-recognition-2017-3",
    "greycite-canonical-uri": "http://uk.businessinsider.com/ibm-edges-closer-to-human-speech-recognition-2017-3",
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    "issued": {
      "date-parts": [
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          2017,
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        "family": "BI Intelligence",
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    "URL": "https://github.com/greenelab/deep-review",
    "title": "greenelab/deep-review",
    "container-title": "GitHub",
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      "date-parts": [
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          2017
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    "id": "url:https://github.com/greenelab/deep-review"
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  {
    "URL": "http://ana.blogs.com/maestros/2006/11/data_is_the_new.html",
    "title": "Data is the New Oil",
    "container-title": "ANA Marketing Maestros",
    "issued": {
      "date-parts": [
        [
          2006,
          11
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      ]
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    "author": [
      {
        "family": "Palmer",
        "given": "Michael"
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    "URL": "http://hazyresearch.github.io/snorkel/blog/weak_supervision.html",
    "title": "Data Programming: Machine Learning with Weak Supervision",
    "issued": {
      "date-parts": [
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          2016,
          9,
          19
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      ]
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    "author": [
      {
        "given": "Alex",
        "family": "Ratner"
      },
      {
        "given": "Stephen",
        "family": "Bach"
      },
      {
        "given": "Chris",
        "family": "Ré"
      }
    ],
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    "URL": "http://research.google.com/archive/large_deep_networks_nips2012.html",
    "type": "article-journal",
    "title": "Large Scale Distributed Deep Networks",
    "container-title": "Neural Information Processing Systems 2012",
    "issued": {
      "date-parts": [
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          2012,
          12
        ]
      ]
    },
    "author": [
      {
        "given": "Jeffrey",
        "family": "Dean"
      },
      {
        "given": "Greg S",
        "family": "Corrado"
      },
      {
        "given": "Rajat",
        "family": "Monga"
      },
      {
        "given": "Kai",
        "family": "Chen"
      },
      {
        "given": "Matthieu",
        "family": "Devin"
      },
      {
        "given": "Quoc V",
        "family": "Le"
      },
      {
        "given": "Mark Z",
        "family": "Mao"
      },
      {
        "given": "Marc’Aurelio",
        "family": "Ranzato"
      },
      {
        "given": "Andrew",
        "family": "Senior"
      },
      {
        "given": "Paul",
        "family": "Tucker"
      },
      {
        "given": "Ke",
        "family": "Yang"
      },
      {
        "given": "Andrew Y",
        "family": "Ng"
      }
    ],
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    "id": "url:http://research.google.com/archive/large_deep_networks_nips2012.html"
  },
  {
    "URL": "http://www.deeplearningbook.org/",
    "title": "Deep Learning",
    "issued": {
      "date-parts": [
        [
          2016
        ]
      ]
    },
    "author": [
      {
        "given": "Ian",
        "family": "Goodfellow"
      },
      {
        "given": "Yoshua",
        "family": "Bengio"
      },
      {
        "given": "Aaron",
        "family": "Courville"
      }
    ],
    "type": "webpage",
    "id": "url:http://www.deeplearningbook.org"
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  {
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      "date-time": "2017-05-21T01:42:31Z",
      "timestamp": 1495330951449
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    "issued": {
      "date-parts": [
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          1994
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    "reference-count": 7,
    "publisher": "IEEE",
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   "title": "Domain adaptation for large-scale sentiment classification: A deep learning approach",
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   "abstract": "The exponential increase in the availability of online reviews and recommendations makes sentiment classification an interesting topic in academic and industrial research. Reviews can span so many different domains that it is difficult to gather annotated training data for all of them. Hence, this paper studies the problem of domain adaptation for sentiment classifiers, hereby a system is trained on labeled reviews from one source domain but is meant to be deployed on another. We propose a deep learning approach which learns to extract a meaningful representation for each review in an unsupervised fashion. Sentiment classifiers trained with this high-level feature representation clearly outperform state-of-the-art methods on a benchmark composed of reviews of 4 types of Amazon products. Furthermore, this method scales well and allowed us to successfully perform domain adaptation on a larger industrial-strength dataset of 22 domains. 1.",
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   "title": "Joint Models for Extracting Adverse Drug Events from Biomedical Text",
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   "event-place": "New York, New York, USA",
   "abstract": "Extracting adverse drug events receives much research attention in the biomedical community. Previous work adopts pipeline models, firstly recognizing drug/disease entity mentions and then identifying adverse drug events from drug/disease pairs. In this paper, we investigate joint models for simultaneously extracting drugs, diseases and adverse drug events. Compared with pipeline models, joint models have two main advantages. First, they make use of information integration to facilitate performance improvement; second, they reduce error propagation in pipeline methods. We compare a discrete model and a deep neural model for extracting drugs, diseases and adverse drug events jointly. Experimental results on a standard ADE corpus show that the discrete joint model outperforms a state-of-the-art baseline pipeline significantly. In addition, when discrete features are replaced by neural features, the recall is further improved.",
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       "family": "Li",
       "given": "Fei"
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       "family": "Zhang",
       "given": "Yue"
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       "family": "Zhang",
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     {
       "family": "Ji",
       "given": "Donghong"
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   "id": "url:https://www.cs.toronto.edu/~kriz/learning-features-2009-TR.pdf",
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   "title": "Deepr: A Convolutional Net for Medical Records",
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       "given": "Phuoc",
       "family": "Nguyen",
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       "given": "Truyen",
       "family": "Tran",
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       "given": "Svetha",
       "family": "Venkatesh",
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   "container-title": "IEEE Journal of Biomedical and Health Informatics",
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   "id": "url:http://www.cs.ox.ac.uk/people/yarin.gal/website/thesis/thesis.pdf",
   "type": "thesis",
   "title": "Uncertainty in deep learning",
   "publisher": "PhD thesis, University of Cambridge",
   "genre": "PhD thesis",
   "URL": "http://www.cs.ox.ac.uk/people/yarin.gal/website/thesis/thesis.pdf",
   "author": [
     {
       "family": "Gal",
       "given": "Yarin"
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 {
  "id": "url:https://www.fasebj.org/doi/abs/10.1096/fasebj.30.1_supplement.406.3",
  "type": "article-journal",
  "title": "Utilizing Machine Learning Approaches to Understand the Interrelationship of Diet, the Human Gastrointestinal Microbiome, and Health",
  "container-title": "The FASEB Journal",
  "page": "406.3-406.3",
  "volume": "30",
  "issue": "1_supplement",
  "source": "fasebj.org (Atypon)",
  "abstract": "BackgroundA growing body of literature supports the ability of specific foods and nutrients to impact the gastrointestinal microbiome. However, there is a dearth of knowledge on the interplay of dietary components (e.g. foods and nutrients), gastrointestinal bacteria, and bacterial metabolites. Current analytical approaches limit investigation of these complex interrelations; therefore, further research utilizing modern machine learning methods is needed.ObjectiveWe aimed to fill the gap in knowledge about the interrelationship of diet, the gastrointestinal microbiome, and health by utilizing multivariate approaches that address P>>N, many features but few samples to 1) validate results generated by prototype software with previously published results; and 2) identify novel associations among relevant foods, nutrients, bacteria, and bacterial metabolites.MethodsData generated from a human dietary intervention study that included habitual food and nutrient intake patterns (NHANES food frequency questionnaire), daily dietary intake records, breath gas, and fecal bacteria and metabolite data were analyzed using machine learning approaches. Relevant bacteria, bacterial metabolites, foods, and nutrients were identified using Random Forest and relationships among these relevant features were determined using Maximal Information Coefficient.ResultsBreath hydrogen and agave inulin supplementation were predictive of Bifidobacterium abundance. Habitual diet factors were highly relevant in predicting participant body mass index (BMI). Among bacteria, Phascolarctobacterium, Collinsella, and Erysipelotrichacea were important features for predicting BMI. Oscillospira was associated with habitual dietary glycemic load. Phascolarctobacterium and Eubacterium were associated with habitual intake of deep yellow vegetables. Dietary polyunstaruated fatty acids were relevant in predicting Bacteroidetes to Firmicutes ratios. Total fiber and butyrate were highly relevant in predicting Faecalibacterium abundance.ConclusionsFindings revealed similar associations among diet, bacterial genera, and breath hydrogen measures as previously published results, thereby validating the use of these methods in diet-microbiome studies. BMI, bacterial abundances, dietary fiber intake, glycemic load, and deep yellow vegetable intake were interrelated. As features of a high quality diet were relevant for predicting bacterial abundances and metabolites, additional work is ongoing to calculate healthy eating index scores. These scores will help delineate purported health benefits (or detriments) of dietary components, bacteria, and bacterial metabolites and aid in identifying targets for future diet-microbiome studies.Support or Funding InformationThis study was partially funded by the Illinois Applied Research Institute.",
  "URL": "https://www.fasebj.org/doi/abs/10.1096/fasebj.30.1_supplement.406.3",
  "ISSN": "0892-6638",
  "journalAbbreviation": "The FASEB Journal",
  "author": [
    {
      "family": "Guetterman",
      "given": "Heather"
    },
    {
      "family": "Auvil",
      "given": "Loretta"
    },
    {
      "family": "Russell",
      "given": "Nate"
    },
    {
      "family": "Welge",
      "given": "Michael"
    },
    {
      "family": "Berry",
      "given": "Matt"
    },
    {
      "family": "Gatzke",
      "given": "Lisa"
    },
    {
      "family": "Bushell",
      "given": "Colleen"
    },
    {
      "family": "Holscher",
      "given": "Hannah"
    }
  ],
  "issued": {
    "date-parts": [
      [
        2016,
        4,
        1
      ]
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}
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