Microarray data classifier consisting of k-top-scoring rank-comparison decision rules with a variable number of genes

Authors:
Youngmi Yoon;Sangjay Bien;Sanghyun Park
Affiliations:
Department of Information Technology, Gachon University of Medicine and Science, Incheon, Korea;Interdisciplinary Program in Bioinformatics, Seoul National University, Seoul, Korea;Department of Computer Science,Yonsei University, Seoul, Korea
Venue:
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
Year:
2010

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Abstract

Microarray experiments generate quantitative expression measurements for thousands of genes simultaneously, which is useful for phenotype classification of many diseases. Our proposed phenotype classifier is an ensemble method with k-topscoring decision rules. Each rule involves a number of genes, a rank comparison relation among them, and a class label. Current classifiers, which are also ensemble methods, consist of k-top-scoring decision rules. Some of these classifiers fix the number of genes in each rule as a triple or a pair. In this paper, we generalize the number of genes involved in each rule. The number of genes in each rule ranges from 2 to N, respectively. Generalizing the number of genes increases the robustness and the reliability of the classifier for the class prediction of an independent sample. Our algorithm saves resources by combining shorter rules in order to build a longer rule. It converges rapidly toward its high-scoring rule list by implementing several heuristics. The parameter k is determined by applying leave-one-out cross validation to the training dataset.