LEAF: Leave-One-Out Forward Selection Method for Cancer Classification Using Gene Expression Data

Abstract

Recent progress of bioinformatics technology has enabled large-scale screening of biomarker candidates. In this paper, we propose a new method called LEAF: LEAve-one-out Forward selection method for analysis of the gene expression data. Our proposed method has made it possible to construct the ranking of informative genes using the parameter which evaluates the efficiency of the class discriminant called Discriminant Power Score (DPS). We apply the LEAF to the three kinds of leukemia dataset (ALL/AML, ALL/MLL and MLL/AML), in a public database. Consequently, our method showed a stable discriminant result with 100% accuracy by the discriminant model which used the three genes set. Furthermore, it was shown that some genes with high DPS are genes related to the cancer clarified by research in recent years. In conclusion, our class discriminant method provides a high accuracy and simply result and supports discovery of a new biomarker. Our compatible method (LEAF) will be a useful tool for many researchers engaged in bioinformatics.