Title :
Discover Gene Specific Local Co-regulations Using Progressive Genetic Algorithm
Author :
Zhang, Ji ; Gao, Qigang ; Wang, Hai
Author_Institution :
Fac. of Comput. Sci., Dalhousie Univ., Halifax, NS
Abstract :
The problem of gene specific co-regulation discovery is that, for a particular gene of interest, identify its closely coregulated genes and the associated subsets of experimental conditions in which such co-regulations occur. The coregulations are local in the sense that they occur in some subsets of full experimental conditions. In this paper, we propose an innovative method for finding gene specific coregulations using genetic algorithm (GA). Two novel ad hoc GAs, the single-stage and two-stage progressive GA, are proposed. They are called progressive because the initial population for the GA in a window position inherits the top-ranked individuals obtained in the preceding window position, enabling them to achieve better accuracy than the nonprogressive algorithm. Experimental results with real-life gene expression data demonstrate the efficiency and effectiveness of our technique in discovering gene specific coregulations
Keywords :
biology computing; genetic algorithms; genetics; GA initial population; coregulated genes; gene expression data; gene specific coregulation discovery; gene specific local coregulations; nonprogressive algorithm; progressive genetic algorithm; single stage progressive GA; two stage progressive GA; Artificial intelligence; Computer science; DNA; Displays; Gene expression; Genetic algorithms; Humans; Nearest neighbor searches; Regulators; Sampling methods;
Conference_Titel :
Tools with Artificial Intelligence, 2006. ICTAI '06. 18th IEEE International Conference on
Conference_Location :
Arlington, VA
Print_ISBN :
0-7695-2728-0
DOI :
10.1109/ICTAI.2006.51