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2009 IEEE International Conference on Bioinformatics and Biomedicine
Microarray Biclustering with Crowding Based MOACO
Washington, D.C., USA
November 01-November 04
ISBN: 978-0-7695-3885-3
Biclustering methods allow us to identify genes withsimilar behavior with respect to different conditions. AntColony Optimization (ACO) algorithms have been shownto be effective problem solving strategies for MultipleObjective Optimization (MOO). Multiple Objective Antcolony optimization (MOACO) mainly focuses on solvingthe multiple objective combinatorial optimizationproblems. This paper incorporates crowding updatetechnology into MOACOB and proposes a novel crowdingbased MOACO biclustering algorithm to mine biclustersfrom microarray dataset. Experimental results are shownfor biclustering algorithm on two real gene expressiondataset.
Citation:
Junwan Liu, Zhoujun Li, Xiaohua Hu, Yiming Chen, "Microarray Biclustering with Crowding Based MOACO," bibm, pp.170-173, 2009 IEEE International Conference on Bioinformatics and Biomedicine, 2009
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