Informative Gene Discovery in DNA Microarray Data Using Statistical Approach
Preventing, diagnosing, and treating disease is greatly facilitated by the availability of biomarkers. Recent improvements in bioinformatics technology have facilitated large-scale screening of DNA microarrays for candidate biomarkers. Here we discuss a gene selection method, which is called LEAve-one-out Forward selection method (LEAF), for discovering informative genes embedded in gene expression data, and propose an additional algorithm for extending LEAF’s capabilities. LEAF is an iterative forward selection method incorporating the concept of leave-one-out cross validation (LOOCV) and provides a discrimination power score (DPS) for genes, which is a criterion for selecting the candidate of informative genes. We show that LEAF identifies genes that are practically used as biomarkers. Our method should be useful bioinformatics tool for biomedical, clinical, and pharmaceutical researchers.
KeywordsBiomarkers Data mining Gene expression profiles Cancer classification
A part of this work was supported by Promotion for Young Research Talent and Network from Northern Advancement Center for Science & Technology (NOASTEC Japan) and Grant-in-Aid for Young Scientists (B) No.21700233 from MEXT Japan.
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