Source code and data for "GADRP: graph convolutional networks and autoencoders for cancer drug response prediction"
- 269-dim-physicochemical.csv - Physicochemical properties of drugs
- 881-dim-fingerprint.csv - Molecular fingerprint of drugs
- miRNA_470cell_734dim.csv - MicroRNA expression data of cell lines
- CpG_407cell_69641dim.csv - DNA methylation data of cell lines
- RNAseq_462cell_48392dim.csv - Gene expression data of cell lines
- copynumber_461cell_23316dim.csv - DNA copy number data of cell lines
- drug_cell_response.csv - response data between drugs and cell lines
- cell_name.csv - Names of 388 cell lines with four cell line characteristics
- drug.py: generate drug similarity matrix according to physicochemical properties of drugs
- cell.py: generate cell line similarity matrix according to microRNA expression and DNA methylation of cell lines
- drug_cell.py: generate drug cell line pairs similarity matrix
- cell_ae.py: learn low_dimensional representations from high-dimensional cell line features
- train.py: train the model and make predictions
- GADRP.py: details of GADRP model
- Python == 3.7.10
- PyTorch == 1.9.0
- sklearn == 0.24.2
- Numpy == 1.19.2
- Pandas == 1.3.4
- Install dependencies, including torch1.9, sklearn, numpy and pandas
- run drug.py and cell.py to generate drug and cell line similarity matrices
- run drug_cell.py to generate drug cell line pair similarity matrix
- run cell_ae.py to generate low-dimensional representations of cell line omics characteristics
- run python train.py for training and prediction
git clone https://github.com/flora619/GADRP