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The Scientific World Journal
Volume 2014, Article ID 593503, 7 pages
http://dx.doi.org/10.1155/2014/593503
Research Article

Evolutionary Approach for Relative Gene Expression Algorithms

Faculty of Computer Science, Bialystok University of Technology, Wiejska 45a, 15-351 Białystok, Poland

Received 6 December 2013; Accepted 24 February 2014; Published 23 March 2014

Academic Editors: S. Balochian, V. Bhatnagar, and Y. Zhang

Copyright © 2014 Marcin Czajkowski and Marek Kretowski. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

A Relative Expression Analysis (RXA) uses ordering relationships in a small collection of genes and is successfully applied to classiffication using microarray data. As checking all possible subsets of genes is computationally infeasible, the RXA algorithms require feature selection and multiple restrictive assumptions. Our main contribution is a specialized evolutionary algorithm (EA) for top-scoring pairs called EvoTSP which allows finding more advanced gene relations. We managed to unify the major variants of relative expression algorithms through EA and introduce weights to the top-scoring pairs. Experimental validation of EvoTSP on public available microarray datasets showed that the proposed solution significantly outperforms in terms of accuracy other relative expression algorithms and allows exploring much larger solution space.