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B3Clustering: Identifying Protein Complexes from Protein-Protein Interaction Network

  • Eunjung Chin
  • Jia Zhu
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7808)

Abstract

Cluster analysis is one of most important challenges for data mining in the modern Biology. The advance of experimental technologies have produced large amount of binary protein-protein interaction data, but it is hard to find protein complexes in vitro.We introduce new algorithm called B3Clustering which detects densely connected subgraphs from the complicated and noisy graph.

B3Clustering finds clusters by adjusting the density of subgraphs to be flexible according to its size, because the more vertices the cluster has, the less dense it becomes. B3Clustering bisects the paths with distance of 3 into two groups to select vertices from each group.We experiment B3Clustering and two other clustering methods in three different PPI networks. Then, we compare the resultant clusters from each method with benchmark complexes called CYC2008. The experimental result supports the efficiency and robustness of B3Clustering for protein complex prediction in PPI networks.

Keywords

Protein Complex Maximal Clique Protein Interaction Network Predict Protein Complex Detect Protein Complex 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Eunjung Chin
    • 1
  • Jia Zhu
    • 1
  1. 1.School of ITEEThe University of QueenslandAustralia

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