title: FM-GA and CM-GA for gene microarray analysis.
abstract: In this paper, we propose two new approaches, FM-GA and CM-GA, to identify significant genes from microarray datasets. FM-GA and CM-GA combine our innovative FM-test and CM-test with genetic algorithm (GA), respectively, and leverage the strengths of GA. The performance of FM-GA and CM-GA was evaluated by the classification accuracy of decision trees constructed with the selected genes. Experiments were conducted to demonstrate the superiority of the proposed method over other approaches.
authors.affiliation: Department of Computer Science and Information Technology, University of the District of Columbia, Washington, DC 20008, USA. LLiang@udc.edu
authors.forename: Lily R
authors.initials: LR
authors.lastname: Liang
authors.forename: Rommel A Benites
authors.initials: RA
authors.lastname: Palomino
authors.forename: Zhao
authors.initials: Z
authors.lastname: Lu
authors.forename: Vinay
authors.initials: V
authors.lastname: Mandal
authors.forename: Deepak
authors.initials: D
authors.lastname: Kumar
mesh_terms: D000465:Algorithms; D016000:Cluster Analysis; D019295:Computational Biology; D030541:Databases, Genetic; D003920:Diabetes Mellitus / Q000235:genetics; D017143:Fuzzy Logic; D020022:Genetic Predisposition to Disease; D006801:Humans; D009369:Neoplasms / Q000235:genetics; D020411:Oligonucleotide Array Sequence Analysis / Q000706:statistics & numerical data*; D009857:Oncogenes