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
DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/BIBM.2009.23
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 Usage of this product signifies your acceptance of the Terms of Use. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||