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Statistical Applications in Genetics and Molecular Biology

Editor-in-Chief: Sanguinetti, Guido


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1544-6115
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Volume 6, Issue 1

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Volume 1 (2002)

Selection of Biologically Relevant Genes with a Wrapper Stochastic Algorithm

Kim-Anh Lê Cao / Olivier Gonçalves / Philippe Besse / Sébastien Gadat
Published Online: 2007-11-06 | DOI: https://doi.org/10.2202/1544-6115.1312

We investigate an important issue of a meta-algorithm for selecting variables in the framework of microarray data. This wrapper method starts from any classification algorithm and weights each variable (i.e. gene) relative to its efficiency for classification. An optimization procedure is then inferred which exhibits important genes for the studied biological process.Theory and application with the SVM classifier were presented in Gadat and Younes, 2007 and we extend this method with CART. The classification error rates are computed on three famous public databases (Leukemia, Colon and Prostate) and compared with those from other wrapper methods (RFE, lo norm SVM, Random Forests). This allows the assessment of the statistical relevance of the proposed algorithm. Furthermore, a biological interpretation with the Ingenuity Pathway Analysis software outputs clearly shows that the gene selections from the different wrapper methods raise very relevant biological information, compared to a classical filter gene selection with T-test.

Keywords: gene selection; classification; stochastic algorithm; cancer databases

About the article

Published Online: 2007-11-06


Citation Information: Statistical Applications in Genetics and Molecular Biology, Volume 6, Issue 1, ISSN (Online) 1544-6115, ISSN (Print) 2194-6302, DOI: https://doi.org/10.2202/1544-6115.1312.

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[1]
Kim-Anh Lê Cao, Emmanuelle Meugnier, and Geoffrey J. McLachlan
Bioinformatics, 2010, Volume 26, Number 9, Page 1192
[2]
Kim-Anh Lê Cao, Agnès Bonnet, and Sébastien Gadat
Computational Statistics & Data Analysis, 2009, Volume 53, Number 10, Page 3601

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