Dual-Dropout Graph Convolutional Network for Predicting Synthetic Lethality in Human Cancers🔥

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Requirements

1. Python == 3.7
2. PyTorch == 1.1.0
3. Numpy == 1.16.2
4. Pandas == 0.24.2
5. SciPy == 1.2

Repository Structure

• Dual-DropoutGCN/data/sl/:
  • ./List_Proteins_in_SL.txt: Contain all the genes involved in SL pairs in our dataset.
  • ./SL_Human - Raw Data.xlsx: Contain original data of SL pairs (including the source of data).
  • ./SL_Human_Approved.txt: Contain SL pairs processed from the "sl_human - raw data.xlsx" file, denoted as SynLethDB.
  • ./computational_pairs.txt: Contain the predicted SLs collected from the "SL_Human - Raw Data.xlsx" file, which will be used to remove them from "SL_Human_Approved.txt" file.
• Dual-DropoutGCN/ddgcn/:
  • ./preprocess.py: Load and preprocess data (e.g., normalization).
  • ./objective.py: Objective function (loss function) of DDGCN model.
  • ./model.py: Implementation of DDGCN model.
  • ./train.py: Train DDGCN. In train.py, if the global variable "NOT_COMPUTATIONAL = True", DDGCN will run on the SynLethDB-NonPred dataset, otherwise it will run on the SynLethDB dataset.
  • ./evaluate.py: Evaluate DDGCN.
  • ./case_study.py: Qualitative case studies.

How to run our code

run the following scripts within Dual-DropoutGCN/ddgcn/

1. ./train.py # Train and evaluate the DDGCN model.
2. ./case_study.py # Train our DDGCN using all the SL pairs in SynLethDB and predict novel SLs from the unknown pairs.

Citation

If you find this useful for your research, we would be appreciated if you cite the following papers:

@article{10.1093/bioinformatics/btaa211,
    author = {Cai, Ruichu and Chen, Xuexin and Fang, Yuan and Wu, Min and Hao, Yuexing},
    title = "{Dual-Dropout Graph Convolutional Network for Predicting Synthetic Lethality in Human Cancers}",
    journal = {Bioinformatics},
    year = {2020},
    month = {03},
    issn = {1367-4803},
    doi = {10.1093/bioinformatics/btaa211},
    url = {https://doi.org/10.1093/bioinformatics/btaa211},
    note = {btaa211},
    eprint = {https://academic.oup.com/bioinformatics/advance-article-pdf/doi/10.1093/bioinformatics/btaa211/32977364/btaa211.pdf},
}