title: Robust and stable gene selection via Maximum-Minimum Correntropy Criterion.
abstract: One of the central challenges in cancer research is identifying significant genes among thousands of others on a microarray. Since preventing outbreak and progression of cancer is the ultimate goal in bioinformatics and computational biology, detection of genes that are most involved is vital and crucial. In this article, we propose a Maximum-Minimum Correntropy Criterion (MMCC) approach for selection of informative genes from microarray data sets which is stable, fast and robust against diverse noise and outliers and competitively accurate in comparison with other algorithms. Moreover, via an evolutionary optimization process, the optimal number of features for each data set is determined. Through broad experimental evaluation, MMCC is proved to be significantly better compared to other well-known gene selection algorithms for 25 commonly used microarray data sets. Surprisingly, high accuracy in classification by Support Vector Machine (SVM) is achieved by less than 10 genes selected by MMCC in all of the cases.
authors.affiliation: Department of Computer Engineering, Faculty of Engineering, Ferdowsi University of Mashhad, Mashhad, Iran.
authors.forename: Majid
authors.initials: M
authors.lastname: Mohammadi
authors.affiliation: Department of Computer Engineering, Faculty of Engineering, Ferdowsi University of Mashhad, Mashhad, Iran; Center of Excellence on Soft Computing and Intelligent Information Processing (SCIIP), Iran.
authors.forename: Hossein
authors.initials: H
authors.lastname: Sharifi Noghabi
authors.affiliation: Department of Electrical Engineering, Faculty of Engineering, Ferdowsi University of Mashhad, Mashhad, Iran. Electronic address: hodtani@um.ac.ir.
authors.forename: Ghosheh
authors.initials: G
authors.lastname: Abed Hodtani
authors.affiliation: Department of Computer Engineering, Faculty of Engineering, Ferdowsi University of Mashhad, Mashhad, Iran; Center of Excellence on Soft Computing and Intelligent Information Processing (SCIIP), Iran; Department of Electrical Engineering, Faculty of Engineering, Ferdowsi University of Mashhad, Mashhad, Iran.
authors.forename: Habib
authors.initials: H
authors.lastname: Rajabi Mashhadi
mesh_terms: D000465:Algorithms; D019295:Computational Biology / Q000379:methods*; D020869:Gene Expression Profiling / Q000379:methods*; D020022:Genetic Predisposition to Disease; D006801:Humans; D009369:Neoplasms / Q000235:genetics*; D010363:Pattern Recognition, Automated; D060388:Support Vector Machine
keywords: Correntropy; Gene selection; Microarray