Skip to content

Causal Relation Extraction Dataset on Genes-Diseases based on biomedical literature data

License

Notifications You must be signed in to change notification settings

BIRDSgroup/CRED

Folders and files

NameName
Last commit message
Last commit date

Latest commit

 

History

86 Commits
 
 
 
 
 
 

Repository files navigation

CRED

Causal Relation Extraction Dataset on Genes-Diseases based on biomedical literature data

We have organised this repository as: code, data, embeddings, intermediate results, pre_trained_models.

The repo contains the code files and data used in the paper : https://www.biorxiv.org/content/10.1101/2024.09.17.613424v1

The code folder contains :

-> classification_code.ipynb: This file contains the code for generating the embeddings using BioBERT model and generating the predictions. This file imports interpretation_code.py and k_fold_cv.py

-> cls_classification.ipynb: This file contains the code for generating the CLS and CLS with G-D embeddings.

-> data_augmentation.ipynb: This file contains the code for augmenting the data

-> hyperparameter_tuning.ipynb: This file contains the code for tuning the hyper-parameters for all the models

-> interpretation_code.py: This file contains the code for calculating interpretation score.

-> k_fold_cv.py: This file contains the code for 4-cross validation.

-> CRED_application_code.ipynb: This file contains the code related to applications of CRED

The data folder contains :

-> new_train_data: training data (after augmentation)

-> only_augmented_data: augmented abstracts

-> test_data: test data

-> val_data: validation data All the annotations of the data are done by CRED developers and all the abstracts are taken from Pubtator

Intermediate results folder contains files (importance scores) used for generating various interpretation plots

Pre-trained models folder contains pre-trained SVM and XGBoost models trained on CRED and CDR data

Installation

See requirements.txt file (in the code folder) for the list of dependencies. All the code is written using python language

Getting started

After doing all the required installations, run classification_code.ipynb file. It can generate embeddings and can also take input as pre-trained embeddings. It will generate the classification results.

Reproduction of Results Tables and Figures

Table 2: Running table1_code.ipynb and auprc.ipynb files

Table 3: Run Inter_annotator_agreement.ipynb file

Table 4: Run classification_code.ipynb

Figure 4: Run cls_classification.ipynb

Figure 5,6,7: Run interpretation_graph_new.ipynb

Figure 8,9: Run CRED_application_code.ipynb

About

Causal Relation Extraction Dataset on Genes-Diseases based on biomedical literature data

Resources

License

Stars

Watchers

Forks

Releases

No releases published

Packages

No packages published