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Transactions of the Institute of Measurement and Control
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A New Algorithm Based on Support Vectors and Penalty Strategy for Identifying Key Genes Related with Cancer

Hong-Qiang Wang

Intellegent Computational Lab., Hefei Institute of Intelligent Machines, Chinese Academy of Sciences, PO Box 1130, Hefei, Anhui, 230031, China, Department of Automation, University of Science and Technology of China, 230026, Hefei, Anhui, China

Kang Li

School of Electronics, Electronic Engineering & Computer Science, Queen’s University, Belfast, UK, k.li{at}qub.ac.uk

This paper proposes a new gene selection algorithm based on support vectors and penalty strategy (SVPS). In detail, cross-validation procedures are performed on training datasets, and in each validation sub-procedure, support vector machines (SVMs) are trained and tested. For each SVM, the support vectors are weighted and combined to form the initial correlation degrees of genes with the class distinction. A penalty strategy is then used to penalize these initial degrees, and the penalized degrees are finally used to produce a criterion for gene selection. The application to the leukaemia dataset shows that the proposed algorithm can identify the key genes related to the class distinction and is competitive to the existing methods.

Key Words: cross-validation • gene selection • leukaemia • penalty strategy • support vectors

Transactions of the Institute of Measurement and Control, Vol. 28, No. 3, 263-273 (2006)
DOI: 10.1191/0142331206tim175oa
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