TIGER: an evolutionary search for Top Inter-GEne Relations

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.