Heterodimeric protein complex identification

研究成果: Chapter in Book/Report/Conference proceedingConference contribution

4 被引用数 (Scopus)

抄録

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.

本文言語英語
ホスト出版物のタイトル2011 ACM Conference on Bioinformatics, Computational Biology and Biomedicine, BCB 2011
ページ499-501
ページ数3
DOI
出版ステータス出版済み - 12 1 2011
イベント2011 ACM Conference on Bioinformatics, Computational Biology and Biomedicine, ACM-BCB 2011 - Chicago, IL, 米国
継続期間: 8 1 20118 3 2011

その他

その他2011 ACM Conference on Bioinformatics, Computational Biology and Biomedicine, ACM-BCB 2011
国/地域米国
CityChicago, IL
Period8/1/118/3/11

All Science Journal Classification (ASJC) codes

  • 生体医工学
  • 健康情報学
  • 健康情報管理

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