Corpus ID: 16128973

Effectively Integrating Information Content and Structural Relationship to Improve the GO-based Similarity Measure Between Proteins

  title={Effectively Integrating Information Content and Structural Relationship to Improve the GO-based Similarity Measure Between Proteins},
  author={Bo Li and F. Luo and J. Z. Wang and F. A. Feltus and J. Zhou},
  • Bo Li, F. Luo, +2 authors J. Zhou
  • Published in BIOCOMP 2010
  • Computer Science, Biology, Mathematics
  • The Gene Ontology (GO) provides a knowledge base to effectively describe proteins. However, measuring similarity between proteins based on GO remains a challenge. In this paper, we propose a new similarity measure, information coefficient similarity measure (SimIC), to effectively integrate both the information content (IC) of GO terms and the structural information of GO hierarchy to determine the similarity between proteins. Testing on yeast proteins, our results show that SimIC efficiently… CONTINUE READING
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