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

Authors:
Pitak Sootanan;Asawin Meechai;Santitham Prom-on;Jonathan H. Chan
Affiliations:
Individual Based Program (Bioinformatics), King Mongkut's University of Technology Thonburi, Bangkok, Thailand;Department of Chemical Engineering, King Mongkut's University of Technology Thonburi, Bangkok, Thailand;Department of Computer Engineering, King Mongkut's University of Technology Thonburi, Bangkok, Thailand;School of Information Technology, King Mongkut's University of Technology Thonburi, Bangkok, Thailand
Venue:
ICONIP'11 Proceedings of the 18th international conference on Neural Information Processing - Volume Part I
Year:
2011

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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.