bens1320 / ddimdl

DDIMDL

DDIMDL builds multimodal deep learning framework with multiple features of drugs to predict drug-drug-interaction(DDI) events.

Usage

Example Usage

    python DDIMDL.py -f smile target enzyme -c DDIMDL -p read

-f featureList: A selection of features to be used in DDIMDL. The optional features are smile(substructure),target,enzyme and pathway of the drugs. It defaults to smile,target and enzyme.
-c classifier: A selection of prediction method to be used. The optional methods are DDIMDL, RF, KNN and LR. It defaults to DDIMDL.
-p NLPProcess: The choices are read and process. It means reading the processed result from database directly or processing the raw data again with NLPProcess.py. It defaults to read. In order to use NLPProcess.py, you need to install StanfordNLP package:

    pip install stanfordnlp

And you need to download english package for StanforNLP:

    import stanfordnlp
    stanfordnlp.download('en')

Dataset

Event.db contains the data we compiled from DrugBank 5.1.3 verision. It has 4 tables:
1.drug contains 572 kinds of drugs and their features.
2.event contains the 37264 DDIs between the 572 kinds of drugs.
3.extraction is the process result of NLPProcess. Each interaction is transformed to a tuple: {mechanism, action, drugA, drugB}
4.event_numer lists the kinds of DDI events and their occurence frequency.

Evaluation

Simply run DDIMDL.py, the train-test procedure will start. The function prepare will calculate the similarity between each drug based on their features.
The function cross_validation will take the feature matrix as input to perform 5-CV and calculate metrics. Two csv files will be generated. For example, smile_all_DDIMDL.csv and smile_each_DDIMDL.csv. The first file evaluates the method's overall performance while the other evaluates the method's performance on each event. The meaning of the metrics can be seen in array result_all and result_eve of DDIMDL.py.

Requirement

• numpy (==1.18.1)
• Keras (==2.2.4)
• pandas (==1.0.1)
• scikit-learn (==0.21.2)
• stanfordnlp (==0.2.0)
  Use the following command to install all dependencies.

    pip install requirement.txt

Notice: Too high version of sklearn will probably not work. We use 0.21.2 for sklearn.

Citation

Please kindly cite the paper if you use the code or the datasets in this repo:

@article{deng2020multimodal,
  title={A multimodal deep learning framework for predicting drug-drug interaction events},
  author={Deng, Yifan and Xu, Xinran and Qiu, Yang and Xia, Jingbo and Zhang, Wen and Liu, Shichao},
  journal={Bioinformatics}
}