Extracting gene regulation information for cancer classification

Authors:
Hong-Qiang Wang;Hau-San Wong;De-Shuang Huang;Jun Shu
Affiliations:
Department of Computer Science, City University of Hong Kong, 83 Tat Chee Ave, Kowloon, Hong Kong;Department of Computer Science, City University of Hong Kong, 83 Tat Chee Ave, Kowloon, Hong Kong;Intelligent Computing Lab, Hefei Institute of Intelligent Machines, Chinese Academy of Science, Hefei, Anhui 230031, China;Division of Respiratory Medicine, Department of Geriatrics, First Affiliated Hospital of Anhui Medical University, Hefei, China
Venue:
Pattern Recognition
Year:
2007

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Abstract

In this paper, we address the problem of extracting gene regulation information from microarray data for cancer classification. From the biological viewpoint, a model of gene regulation probability is established where three types of gene regulation states in a tissue sample are assumed and then two regulation events correlated with the class distinction are defined. Different from the previous approaches, the proposed algorithm uses gene regulation probabilities as carriers of regulation information to select genes and construct classifiers. The proposed approach is successfully applied to two public available microarray data sets, the leukemia data and the prostate data. Experimental results suggest that gene selection based on regulation information can greatly improve cancer classification, and the classifier based on regulation information is more efficient and more stable than several previous classification algorithms.