FM-GA and CM-GA for gene microarray analysis.
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.
Liang
Lily R
LR
Department of Computer Science and Information Technology, University of the District of Columbia, Washington, DC 20008, USA. LLiang@udc.edu
Palomino
Rommel A Benites
RA
Lu
Zhao
Z
Mandal
Vinay
V
Kumar
Deepak
D
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