Corpus ID: 16128973

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

@inproceedings{Li2010EffectivelyII,
  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},
  booktitle={BIOCOMP},
  year={2010}
}

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

View PDF on arXiv

Save to Library

Create Alert

Cite

Launch Research Feed