HKC: An Algorithm to Predict Protein Complexes in Protein-Protein Interaction Networks.

With the availability of more and more genome-scale protein-protein interaction (PPI) networks, research interests gradually shift to Systematic Analysis on these large data sets. A key topic is to predict protein complexes in PPI networks by identifying clusters that are densely connected within th...

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Publicado en:Journal of Biomedicine & Biotechnology pp. 1 - 15
Autores principales: Xiaomin Wang, Zhengzhi Wang, Jun Ye
Formato: Journal Article
Publicado: Wiley-Blackwell 2011
Acceso en línea:Ver este registro en EBSCOhost
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        atl: HKC: An Algorithm to Predict Protein Complexes in Protein-Protein Interaction Networks.
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          Xiaomin Wang
          Zhengzhi Wang
          Jun Ye
        affil: Institute of Mechanical Engineering and Automation, National University of Defense Technology, Changsha 410073, China
      sug:
      ab: With the availability of more and more genome-scale protein-protein interaction (PPI) networks, research interests gradually shift to Systematic Analysis on these large data sets. A key topic is to predict protein complexes in PPI networks by identifying clusters that are densely connected within themselves but sparsely connected with the rest of the network. In this paper, we present a new topology-based algorithm, HKC, to detect protein complexes in genome-scale PPI networks. HKC mainly uses the concepts of highest k-core and cohesion to predict protein complexes by identifying overlapping clusters. The experiments on two data sets and two benchmarks show that our algorithm has relatively high F-measure and exhibits better performance compared with some other methods.
      pubtype: Academic Journal
      doctype: Journal Article
      ougenre: Article
    language: English
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