EGATMDA: Ensembling Graph Attention Networks for Human Microbe-Drug Association prediction.
- drugs: ID and names for drugs.
- microbes: ID and names for microbes.
- diseases: ID and names for diseases.
- adj: interaction pairs between drugs and microbes.
- drug_microbe_associations: associations between drugs and microbes.
- drug_disease_associations: associations between drugs and diseases.
- microbe_disease_associations: associations between microbesa and diseases.
- drug_drug_interaction: interactions between drugs.
- microbe_microbe_interaction: interactions between microbes.
- drug_features: pre-processing feature matrix for drugs.
- microbe_features: genome sequence feature matrix for microbes.
- interaction: known adjacent matrix for drugs and microbes.
- net1: known adjacent matrix for drugs and microbes, i.e., interaction.
- net2: virtual adjacent matrix for drugs and microbes obtained from network Net2.
- net3: virtual adjacent matrix for drugs and microbes obtained from network Net3.
- net123: integrated adjacent matrix for drugs and microbes by fusing net1, net1 and net3.
- To generate training data and test data.
- Run main.py to train the model and obtain the predicted scores for microbe-drug associations.
- EGATMDA is implemented to work under Python 3.7.
- Tensorflow
- numpy
- scipy
- sklearn