Comparison of five supervised feature selection algorithms leading to top features and gene signatures from multi-omics data in cancer.

We performed a comprehensive comparison of the performance evaluation of five well-known feature selection methods for mining features from various high-dimensional datasets. We identified signature genes using supervised learning for the specific omic data for the disease. The study will help incorporate higher order dependencies among features.