A hybrid gene selection method based on gene scoring strategy and improved particle swarm optimization

Abstract

Background Gene selection is one of the critical steps in the course of the classification of microarray data. Since particle swarm optimization has no complicated evolutionary operators and fewer parameters need to be adjusted, it has been used increasingly as an effective technique for gene selection. Since particle swarm optimization is apt to converge to local minima which lead to premature convergence, some particle swarm optimization based gene selection methods may select non-optimal genes with high probability. To select predictive genes with low redundancy as well as not filtering out key genes is still a challenge. Results To obtain predictive genes with lower redundancy as well as overcome the deficiencies of traditional particle swarm optimization based gene selection methods, a hybrid gene selection method based on gene scoring strategy and improved particle swarm optimization is proposed in this paper. To select the genes highly related to out samples’ classes, a gene scoring strategy based on randomization and extreme learning machine is proposed to filter much irrelevant genes. With the third-level gene pool established by multiple filter strategy, an improved particle swarm optimization is proposed to perform gene selection. In the improved particle swarm optimization, to decrease the likelihood of the premature of the swarm the Metropolis criterion of simulated annealing algorithm is introduced to update the particles, and the half of the swarm are reinitialized when the swarm is trapped into local minima. Conclusions Combining the gene scoring strategy with the improved particle swarm optimization, the new method could select functional gene subsets which are significantly sensitive to the samples’ classes. With the few discriminative genes selected by the proposed method, extreme learning machine and support vector machine classifiers achieve much high prediction accuracy on several public microarray data, which in turn verifies the efficiency and effectiveness of the proposed gene selection method.

An efficient gene selection method for microarray data based on LASSO and BPSO

“…And the Brain and Lymphoma data are available at: http://linus.nci.nih.gov/~brb/DataArchive_New.html [16]. The detailed specification of the datasets is the same as in [30].…”

“…The experiments are carried out 500 times on each microarray data, and the top ten frequently selected genes by the proposed method are listed in Tables 2, 3, 4, 5, and 6. Many genes selected by the KL-IBPSO-ELM method were also selected by one or more methods proposed in [16, 17] [30–34].…”

“…Many genes selected by the KL-IBPSO-ELM method were also selected by one or more methods proposed in [16, 17] [30–34].…”

“…#✺ 251U37012Hunman cleavage and polyadenylation specificity factor mRNA,complete cds. ※ 554H24401MAP KINASE PHOSPHATASE-1 ( Homo sapiens ) 175T94579Human chitotriosidase precursor mRNA, complete cds #※✺ 1346T6294760S RIBOSOMAL PROTEIN L24 ( Arabidopsis thaliana ) 1771J05032Human aspartyl-tRNA synthetase alpha-2 subunit mRNA, complete cds✺ 765M76378Human cysteine-rich protein (CPR) gene, exons 5 and 6 #✺ 1902U01038Human pLK mRNA, complete cds※also selected in [16]; # also selected in [17]; ❖also selected in [31]; ✺also selected in [32];✱also selected in [30]…”

“…Additionally, the proposed gene selection method contains filtering irrelevant genes to establish the gene pool and using PSO to select functional gene subsets from the gene pool, and its computational complexity is at the same order of magnitude of that of the PSO-based [16, 17, 30] gene selection methods.…”