Gene selection for classification of microarray data based on the Bayes error |
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| Authors: | Ji-Gang Zhang Hong-Wen Deng |
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| Institution: | (1) Laboratory of Molecular and Statistical Genetics, College of Life Sciences, Hunan Normal University, Changsha, Hunan, 410081, P. R. China;(2) The Key Laboratory of Biomedical Information Engineering of Ministry of Education and Institute of Molecular Genetics, School of Life Science and Technology, Xi'an Jiaotong University, Xi'an, 710049, P. R. China;(3) Departments of Orthopedic Surgery and Basic Medical Science, School of Medicine, University of Missouri-Kansas City, 2411 Holmes Street, Kansas City, MO 64108, USA |
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| Abstract: | Background With DNA microarray data, selecting a compact subset of discriminative genes from thousands of genes is a critical step for
accurate classification of phenotypes for, e.g., disease diagnosis. Several widely used gene selection methods often select
top-ranked genes according to their individual discriminative power in classifying samples into distinct categories, without
considering correlations among genes. A limitation of these gene selection methods is that they may result in gene sets with
some redundancy and yield an unnecessary large number of candidate genes for classification analyses. Some latest studies
show that incorporating gene to gene correlations into gene selection can remove redundant genes and improve classification
accuracy. |
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