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Bioinformatics Advance Access published online on October 9, 2007
Bioinformatics, doi:10.1093/bioinformatics/btm429
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© The Author (2007). Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oxfordjournals.org

Feature construction from synergic pairs to improve microarray-based classification

Blaise Hanczar a,*, Jean-Daniel Zucker a,b, Corneliu Henegar b and Lorenza Saitta c

aLaboratoire d'Informatique Médicale et Bioinformatique (Lim&Bio), Université Paris 13, 93017 Bobigny, FRANCE,bInserm U755 Nutriomique, Hôpital Hôtel-Dieu, 75004 Paris, FRANCE, cDipartimento di Informatica, Università del Piemonte Orientale, 15100 Alessandria, ITALY

*To whom correspondence should be addressed. Blaise Hanczar, E-mail: hanczar_blaise{at}yahoo.fr


  Abstract

Motivation: Microarray experiments which allow simultaneous expression profiling of thousands of genes in various conditions (tissues, cells or time) generate data whose analysis raises difficult problems. In particular there is a vast disproportion between the number of attributes (tens of thousands) and the number of examples (several tens). Dimension reduction is therefore a key step before applying classification approaches. Many methods have been proposed to this purpose, but only a few of them considered a direct quantification of transcriptional interactions. We describe and experimentally validate a new dimension reduction and feature construction method, which assesses interactions between expression profiles to improve microarray-based classification accuracy.

Results: Our approach relies on a mutual information measure that exposes some elementary constituents of the information contained in a pair of gene expression profiles. We show that their analysis implies a term which represents the information of the interaction between the two genes. The principle of our method, called FeatKNN, is to exploit the information provided by highly synergic gene pairs to improve classification accuracy. First, a heuristic search selects the most informative gene pairs. Then, for each selected pair, a new feature, representing the classification margin of a KNN classifier in the gene pairs space, is constructed. We show experimentally that the interactional information has a degree of significance comparable to that of the gene expression profiles considered separately. Our method has been tested with different classifiers and yielded significant improvements in accuracy on several public microarray databases. Moreover, a synthetic assessment of the biological significance of the concept of synergic gene pairs suggested its ability to uncover relevant mechanisms underlying interactions among various cellular processes.

Supplementary information: Companion Website http://featknn.nutriomique.org

Contact: hanczar_blaise{at}yahoo.fr

Associate Editor: Dr. Joaquin Dopazo

Received on February 5, 2007; revised on July 6, 2007; accepted on August 18, 2007

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