Multi-variable AUC for Sifting Complementary Features and Its Biomedical Application

Data

1. UCI data and multi-class data were downloaded from UC Irvine Machine Learning Repository
2. TCGA datasets are preprocessed data from the Xena platform in the “gene expression RNAseq -IlluminaHiSeq pancan normalized” version. The file is too large to upload on Github. Please see details and download datasets on the provided web links.

• BRCA (TCGA Breast Cancer)
• KIRC (TCGA Kidney Clear Cell Carcinoma)
• LIHC (TCGA Liver Cancer)
• LUAD (TCGA Lung Adenocarcinoma)
• PRAD (TCGA Prostate Cancer)
• THCA (TCGA Thyroid Cancer)

Code

1. data_process.py is code for data preprocessing.
2. feature_selection.py is code for sifting an optimal feature set.
3. feature_ranking.py is code for ranking features from high to low and also sorting features with their frequency.

Contributions of this algorithm:

1. Globally evaluate the complementarity among features.
2. Screen discriminative combination of features that are complementary to each other from a global view.

Note: features mean genes when applied to gene expression datasets.