Evolutionary approach for relative gene expression algorithms.
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.
Faculty of Computer Science, Bialystok University of Technology, Wiejska 45a, 15-351 Białystok, Poland.
Marcin
M
Czajkowski
Faculty of Computer Science, Bialystok University of Technology, Wiejska 45a, 15-351 Białystok, Poland.
Marek
0000-0001-9175-2678
M
Kretowski
D000465:Algorithms; D019295:Computational Biology / Q000379:methods*; D019143:Evolution, Molecular; D020869:Gene Expression Profiling / Q000145:classification / Q000379:methods* / Q000706:statistics & numerical data; D056084:Genetic Fitness; D014644:Genetic Variation; D009154:Mutation; D020411:Oligonucleotide Array Sequence Analysis / Q000145:classification / Q000379:methods* / Q000706:statistics & numerical data; D011995:Recombination, Genetic; D012641:Selection, Genetic