bigchengkaixin / indep

InDEP: an interpretable machine learning approach to predict cancer driver genes from multi-omics data

This study aims to address this issue by introducing InDEP, an interpretable machine learning framework based on cascade forests. InDEP is designed with easy-to-interpret features, cascade forests based on decision trees, and a KernelSHAP module that enables fine-grained post-hoc interpretation. Integrating multiomics data, InDEP can identify essential features of classified driver genes at both the gene and cancer-type levels. The framework accurately identifies driver genes, discovers new patterns that make genes as driver genes from the prediction process, and refines the cancer driver gene catalog. In comparison to state-of-the-art methods, InDEP proved to be more accurate on the test set and identified reliable candidate driver genes. Mutational features were the primary drivers for InDEP’s identifying driver genes, with other omics features also contributing. At the gene level, the framework concluded that substitution-type mutations were the main reason most genes were identified as driver genes.

Install

cd InDEP

pip install -r requirement.txt

Usage

cd codes

for example:

train the model

python run.py -t PANCAN -m train -l InDEP

get the score of genes

python run.py -t PANCAN -m score -l InDEP

test the model

python run.py -t PANCAN -m eval -l InDEP

get the interpretation of the model

python run.py -t PANCAN -m interpretation -l InDEP