Article: TIGER: an evolutionary search for Top Inter-GEne Relations Journal: International Journal of Data Mining and Bioinformatics (IJDMB) 2016 Vol.16 No.2 pp.170 - 182 Abstract: Relative Expression Analysis (RXA) plays an important role in biomarker discovery and microarray data classification. It focuses on ordering relationships between the expression of small sets of genes rather than their raw values. Most of the RXA algorithms are preceded by feature selection as analysing all possible subsets of genes is computationally infeasible. In this paper, we propose an efficient solution that unifies major variants of RXA algorithms and is capable of searching top inter-gene relations even in large microarray datasets. A specialised evolutionary algorithm that incorporates and exploits knowledge about RXA into the evolutionary search allows exploring solution space with all available genes. By embedding information about the genes' discriminative power we managed to speed up the evolutionary process and to search for complex interactions between genes. Experimental validation shows that the proposed solution outperforms popular RXA algorithms and has considerable potential for discovering new relationships between the genes. Inderscience Publishers - linking academia, business and industry through research

Title: TIGER: an evolutionary search for Top Inter-GEne Relations

Authors: Marcin Czajkowski; Anna Czajkowska; Marek Kretowski

Addresses: Faculty of Computer Science, Bialystok University of Technology, Wiejska 45a, 15-351 Białystok, Poland ' Department of Biotechnology, Medical University of Bialystok, Kilinskiego 1, 15-089 Białystok, Poland ' Faculty of Computer Science, Bialystok University of Technology, Wiejska 45a, 15-351 Białystok, Poland

Abstract: Relative Expression Analysis (RXA) plays an important role in biomarker discovery and microarray data classification. It focuses on ordering relationships between the expression of small sets of genes rather than their raw values. Most of the RXA algorithms are preceded by feature selection as analysing all possible subsets of genes is computationally infeasible. In this paper, we propose an efficient solution that unifies major variants of RXA algorithms and is capable of searching top inter-gene relations even in large microarray datasets. A specialised evolutionary algorithm that incorporates and exploits knowledge about RXA into the evolutionary search allows exploring solution space with all available genes. By embedding information about the genes' discriminative power we managed to speed up the evolutionary process and to search for complex interactions between genes. Experimental validation shows that the proposed solution outperforms popular RXA algorithms and has considerable potential for discovering new relationships between the genes.

Keywords: relative expression analysis; RXA; top-scoring pair; biomarker discovery; microarray data classification; evolutionary algorithm; embedded feature selection; biomarkers; inter-gene relations; gene interaction; bioinformatics.

DOI: 10.1504/IJDMB.2016.080042

International Journal of Data Mining and Bioinformatics, 2016 Vol.16 No.2, pp.170 - 182

Received: 30 Dec 2015
Accepted: 11 Sep 2016
Published online: 29 Oct 2016 *

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Title: TIGER: an evolutionary search for Top Inter-GEne Relations

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