Pathway-based microarray analysis with negatively correlated feature sets for disease classification

Conference proceedings article

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Publication Details

Author list: Sootanan P., Meechai A., Prom-On S., Chan J.H.

Publisher: Springer

Publication year: 2011

Volume number: 7062 LNCS

Issue number: PART 1

Start page: 676

End page: 683

Number of pages: 8

ISBN: 9783642249549

ISSN: 0302-9743

URL: https://www.scopus.com/inward/record.uri?eid=2-s2.0-81855199811&doi=10.1007%2f978-3-642-24955-6_80&partnerID=40&md5=afc526f95d2622f5ebde54e0e3477d0d

Languages: English-Great Britain (EN-GB)

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Abstract

Accuracy of disease classification has always been a challenging goal of bioinformatics research. Microarray-based classification of disease states relies on the use of gene expression profiles of patients to identify those that have profiles differing from the control group. A number of methods have been proposed to identify diagnostic markers that can accurately discriminate between different classes of a disease. Pathway-based microarray analysis for disease classification can help improving the classification accuracy. The experimental results showed that the use of pathway activities inferred by the negatively correlated feature sets (NCFS) based methods achieved higher accuracy in disease classification than other different pathway-based feature selection methods for two breast cancer metastasis datasets. ฉ 2011 Springer-Verlag.

Keywords

Pathway-based feature selection