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GraphHSA

Code and datasets for "Revealing Herb-Symptom Associations and Mechanisms of Action in Protein Networks Using Subgraph Matching Learning"

Tuturial

To get the experiment results by running the scripts (scenario is warm start or cold start for herb/symptom/pair):

python main_cv.py --save_dir results_cv_{scenario}/ --data_dir ../data/{scenario}/   ###5-fold cross-validation, .pickle file is obtained from save_train_val_test_data.py
python main_indep.py --save_dir results_indep_{scenario}/ --data_dir ../data/{scenario}/   ###independent test

Users can use their own data to train prediction models.

python save_train_val_test_data.py   ###splitting the input data by different strategies

Requirements

  • numpy 1.25.0
  • pandas 1.5.3
  • networkx 3.1
  • scipy 1.10.1
  • scikit-learn 1.2.2
  • pytorch 2.0.1
  • torch-geometric 2.3.1
  • python 3.9.18

Contact

Please feel free to contact us if you need any help ([email protected] or [email protected]).

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