Detecting protein complexes from noisy protein interaction data

Research output: Chapter in Book/Report/Conference proceedingConference contribution

4 Citations (Scopus)

Abstract

High-throughput experimental techniques have made available large datasets of experimentally detected protein-protein interactions. However, experimentally determined protein complexes datasets are not exhaustive nor reliable. A protein complex plays a key role in disease development. Therefore, the identification and characterization of protein complexes involved is crucial to the understanding of the molecular events under normal and abnormal physiological conditions. In this paper, we propose a novel graph mining algorithm to identify protein complexes. The algorithm first checks the quality of the interaction data, then predicts protein complexes based on the concept of weighted clustering coefficient. To demonstrate the effectiveness of our proposed method, we present experimental results on yeast protein interaction data. The level of accuracy achieved is a strong argument in favor of the proposed method. Novel protein complexes were also predicted to assist biologists in their search for protein complexes. The datasets and programs are freely available from http://faculty.uaeu.ac.ae/nzaki/PE-WCC. htm.

Original languageEnglish
Title of host publicationProc. of the 11th Int. Workshop on Data Mining in Bioinformatics, BIOKDD 2012 - Held in Conjunction with the 18th ACM SIGKDD Int. Conference on Knowledge Discovery and Data Mining, SIGKDD'12
Pages1-7
Number of pages7
DOIs
Publication statusPublished - Sep 28 2012
Event11th International Workshop on Data Mining in Bioinformatics, BIOKDD 2012 - Held in Conjunction with the 18th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, SIGKDD'12 - Beijing, China
Duration: Aug 12 2012Aug 12 2012

Publication series

NameProceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining

Other

Other11th International Workshop on Data Mining in Bioinformatics, BIOKDD 2012 - Held in Conjunction with the 18th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, SIGKDD'12
Country/TerritoryChina
CityBeijing
Period8/12/128/12/12

Keywords

  • Clustering coefficient
  • Detecting protein complexes
  • Interaction reliability
  • Protein complex
  • Protein-protein interaction network

ASJC Scopus subject areas

  • Software
  • Information Systems

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