Heterodimeric protein complex identification

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

4 Citations (Scopus)

Abstract

It is a challenging problem to predict heterodimeric protein complexes accurately in size and membership, because, in yeast, those complexes are the majority of curated protein complexes, and structures of those complexes are much simpler than those of complexes consisting of three or more proteins. In this paper, we characterize heterodimeric protein complexes by supervised-learning of a naïve Bayes classifier from heterogeneous genomic data, including protein-protein interaction data, gene expression data, and gene ontology annotations. We have examined predictability of the trained classifier and compared it with those of existing popular protein complex prediction tools. The result shows that our method outperforms the others.

Original languageEnglish
Title of host publication2011 ACM Conference on Bioinformatics, Computational Biology and Biomedicine, BCB 2011
Pages499-501
Number of pages3
DOIs
Publication statusPublished - Dec 1 2011
Event2011 ACM Conference on Bioinformatics, Computational Biology and Biomedicine, ACM-BCB 2011 - Chicago, IL, United States
Duration: Aug 1 2011Aug 3 2011

Other

Other2011 ACM Conference on Bioinformatics, Computational Biology and Biomedicine, ACM-BCB 2011
CountryUnited States
CityChicago, IL
Period8/1/118/3/11

All Science Journal Classification (ASJC) codes

  • Biomedical Engineering
  • Health Informatics
  • Health Information Management

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