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Protein Complex Discovery by Interaction Filtering from Protein Interaction Networks Using Mutual Rank Coexpression and Sequence Similarity.

The evaluation of the biological networks is considered the essential key to understanding the complex biological systems. Meanwhile, the graph clustering algorithms are mostly used in the protein-protein interaction (PPI) network analysis. The complexes introduced by the clustering algorithms inclu...

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Published in:BioMed Research International Vol. 2015; pp. 1 - 8
Main Authors: Kazemi-Pour, Ali, Goliaei, Bahram, Pezeshk, Hamid
Format: equations & formulas research tables/charts Journal Article
Published: Wiley-Blackwell 1/27/2015
Online Access:View this record in EBSCOhost
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        23146133
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      jtl: BioMed Research International
      issn: 23146133
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      dt: 1/27/2015
      vid: 2015
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      pub: Wiley-Blackwell
      place: Malden, Massachusetts
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        109273190
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        109273190
        10.1155/2015/165186
        109273190
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        atl: Protein Complex Discovery by Interaction Filtering from Protein Interaction Networks Using Mutual Rank Coexpression and Sequence Similarity.
      aug:
        au:
          Kazemi-Pour, Ali
          Goliaei, Bahram
          Pezeshk, Hamid
        affil: Institute of Biochemistry and Biophysics, University of Tehran, Enghelab Avenue, P.O. Box 13145-1384, Tehran, Iran
      sug:
        subj:
          Proteins Analysis
          Diagnosis, Laboratory Methods
          In Vitro Studies
          Cell Physiology
          Paired T-Tests
          Sequence Analysis
      ab: The evaluation of the biological networks is considered the essential key to understanding the complex biological systems. Meanwhile, the graph clustering algorithms are mostly used in the protein-protein interaction (PPI) network analysis. The complexes introduced by the clustering algorithms include noise proteins. The error rate of the noise proteins in the PPI network researches is about 40–90%. However, only 30–40% of the existing interactions in the PPI databases depend on the specific biological function. It is essential to eliminate the noise proteins and the interactions from the complexes created via clustering methods. We have introduced new methods of weighting interactions in protein clusters and the splicing of noise interactions and proteins-based interactions on their weights. The coexpression and the sequence similarity of each pair of proteins are considered the edge weight of the proteins in the network. The results showed that the edge filtering based on the amount of coexpression acts similar to the node filtering via graph-based characteristics. Regarding the removal of the noise edges, the edge filtering has a significant advantage over the graph-based method. The edge filtering based on the amount of sequence similarity has the ability to remove the noise proteins and the noise interactions.
      pubtype: Academic Journal
      doctype:
        equations & formulas
        research
        tables/charts
        Journal Article
      ougenre: Article
    language: English
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