MFCluster: mining maximal fault-tolerant constant row biclusters in microarray dataset

Authors:
Miao Wang;Xuequn Shang;Miao Miao;Zhanhuai Li;Wenbin Liu
Affiliations:
School of Computer Science and Engineering, Northwestern Polytechnical University, Xi'an, China;School of Computer Science and Engineering, Northwestern Polytechnical University, Xi'an, China;School of Computer Science and Engineering, Northwestern Polytechnical University, Xi'an, China;School of Computer Science and Engineering, Northwestern Polytechnical University, Xi'an, China;Department of Physics and Electronic information engineering, Wenzhou University, Wenzhou, Zhejiang, China
Venue:
WAIM'11 Proceedings of the 12th international conference on Web-age information management
Year:
2011

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Abstract

Biclustering is one of the most popular methods for microarray dataset analysis, which allows for conditions and genes clustering simultaneously. However, due to the influence of experimental noise in the microarray dataset, using traditional biclustering methods may neglect some significative biological biclusters. In order to reduce the influence of noise and find more types of biological biclusters, we propose an algorithm, MFCluster, to mine fault-tolerant biclusters in microarray dataset. MFCluster uses several novel techniques to generate fault-tolerant efficiently by merging nonrelaxed biclusters. MFCluster generates a weighted undirected relational graph firstly. Then all the maximal fault-tolerant biclusters would be mined by using patterngrowth method in above graph. The experimental results show our algorithm is more efficiently than traditional ones.