A pathway-based classification method that can improve microarray-based colorectal cancer diagnosis

Authors:
Hong-Qiang Wang;Xin-Ping Xie;Chun-Hou Zheng
Affiliations:
Intelligent Computing Lab, Hefei Institute of Physical Science, CAS, Hefei, China;Department of Mathematics and Physics, Anhui University of Architecture, Hefei, China;College of Information and Communication Technology, Qufu Normal University, Rizhao, China
Venue:
ICIC'11 Proceedings of the 7th international conference on Intelligent Computing: bio-inspired computing and applications
Year:
2011

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Abstract

Colorectal cancer is the third most commonly diagnosed cancer in the world. Microarray-based colorectal cancer diagnosis is increasingly paid more and more attentions. In view of a number of pathway information available in the KEGG database, this paper proposes to model pathways for colorectal cancer diagnosis, and as a result, a pathway-based classification method is developed. The proposed method can extract pathway information through modeling gene associations in a pathway via regression. Experimental results on six pathways show that the proposed method remarkably improves the performance of microarray-based colorectal cancer diagnosis.