Brain Cancer Predictive Modeling and Biomarker Discovery Challenge

This is my solution for the precisionFDA Brain Cancer Predictive Modeling and Biomarker Discovery Challenge

The main steps in my solution are as follows:

• Feature selection with L0Learn 'Fast Best Subset Selection' (Hazimeh et al., 2018) for both Gene Expression and CNV data
• Then gradient boosting decision tree models are applied as a predictive model to the selected features with l0learn
• Three different boosting models are used: XGBoost (Chen and Guestrin, 2016), LightGBM (Ke et al., 2017), and CatBoost (Prokhorenkova et al., 2018).