A Novel Gene Analysis Method for Biomarker Mining in DNA Microarray Data

Abstract

Preventing, diagnosing, and treating disease is greatly facilitated by the availability of biomarkers. Recent improvements in bioinformatics technology have facilitated largescale screening of DNA arrays for candidate biomarkers. Here we discuss a gene analysis method that we call the LEAve-oneout Forward selection method (LEAF) for discovering informative genes embedded in expression data, and propose an additional algorithm for extending LEAF’s capabilities. An iterative forward selection method incorporating the concept of leaveone-out cross validation (LOOCV), LEAF provides a discrimination power score (DPS) for genes. We show that LEAF identifies genes that correspond to known biomarkers. Therefore, our method should provide a useful bioinformatics tool for biomedical, clinical, and pharmaceutical researchers.