Protein classification using probabilistic chain graphs and the Gene Ontology structure

Steven Carroll, Vladimir Pavlovic

Research output: Contribution to journalArticlepeer-review

29 Scopus citations

Abstract

Motivation: Probabilistic graphical models have been developed in the past for the task of protein classification. In many cases, classifications obtained from the Gene Ontology have been used to validate these models. In this work we directly incorporate the structure of the Gene Ontology into the graphical representation for protein classification. We present a method in which each protein is represented by a replicate of the Gene Ontology structure, effectively modeling each protein in its own 'annotation space'. Proteins are also connected to one another according to different measures of functional similarity, after which belief propagation is run to make predictions at all ontology terms. Results: The proposed method was evaluated on a set of 4879 proteins from the Saccharomyces Genome Database whose interactions were also recorded in the GRID project. Results indicate that direct utilization of the Gene Ontology improves predictive ability, outperforming traditional models that do not take advantage of dependencies among functional terms. Average increase in accuracy (precision) of positive and negative term predictions of 27.8% (2.0%) over three different similarity measures and three subontologies was observed. Availability: C/C++/Perl implementation is available from authors upon request.

Original languageEnglish (US)
Pages (from-to)1871-1878
Number of pages8
JournalBioinformatics
Volume22
Issue number15
DOIs
StatePublished - Aug 1 2006

All Science Journal Classification (ASJC) codes

  • Statistics and Probability
  • Biochemistry
  • Molecular Biology
  • Computer Science Applications
  • Computational Theory and Mathematics
  • Computational Mathematics

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