title: A top-r feature selection algorithm for microarray gene expression data.
abstract: Most of the conventional feature selection algorithms have a drawback whereby a weakly ranked gene that could perform well in terms of classification accuracy with an appropriate subset of genes will be left out of the selection. Considering this shortcoming, we propose a feature selection algorithm in gene expression data analysis of sample classifications. The proposed algorithm first divides genes into subsets, the sizes of which are relatively small (roughly of size h), then selects informative smaller subsets of genes (of size r < h) from a subset and merges the chosen genes with another gene subset (of size r) to update the gene subset. We repeat this process until all subsets are merged into one informative subset. We illustrate the effectiveness of the proposed algorithm by analyzing three distinct gene expression data sets. Our method shows promising classification accuracy for all the test data sets. We also show the relevance of the selected genes in terms of their biological functions.
authors.affiliation: Laboratory of DNA Information Analysis, Human Genome Center, Institute of Medical Science, University of Tokyo, 4-6-1 Shirokanedai, Minato-ku, Tokyo 108-8639, Japan. aloks@ims.u-tokyo.ac.jp
authors.forename: Alok
authors.initials: A
authors.lastname: Sharma
authors.forename: Seiya
authors.initials: S
authors.lastname: Imoto
authors.forename: Satoru
authors.initials: S
authors.lastname: Miyano
mesh_terms: D000465:Algorithms; D016208:Databases, Factual; D015870:Gene Expression; D020869:Gene Expression Profiling / Q000379:methods; D006801:Humans; D020411:Oligonucleotide Array Sequence Analysis