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MFCluster: Mining Maximal Fault-Tolerant Constant Row Biclusters in Microarray Dataset

  • Miao Wang
  • Xuequn Shang
  • Miao Miao
  • Zhanhuai Li
  • Wenbin Liu
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6897)

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 non-relaxed biclusters. MFCluster generates a weighted undirected relational graph firstly. Then all the maximal fault-tolerant biclusters would be mined by using pattern-growth method in above graph. The experimental results show our algorithm is more efficiently than traditional ones.

Keywords

fault-tolerant bicluster microarray constant row 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Miao Wang
    • 1
  • Xuequn Shang
    • 1
  • Miao Miao
    • 1
  • Zhanhuai Li
    • 1
  • Wenbin Liu
    • 2
  1. 1.School of Computer Science and EngineeringNorthwestern Polytechnical UniversityXi’anChina
  2. 2.Department of Physics and Electronic information engineeringWenzhou UniversityWenzhouChina

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