Background: In Gene Ontology, the "Molecular Function" (MF) categorization is a widely used knowledge framework for gene function comparison and prediction. Its structure and annotation provide a convenient way to compare gene functional similarities at the molecular level. The existing gene similarity measures, however, solely rely on one or few aspects of MF without utilizing all the rich information available including structure, annotation, common terms, lowest common parents. Results: We introduce a rank-based gene semantic similarity measure called InteGO by synergistically integrating the state-of-the-art gene-to-gene similarity measures. By integrating three GO based seed measures, InteGO significantly improves the performance by about two-fold in all the three species studied (yeast, Arabidopsis and human). Conclusions: InteGO is a systematic and novel method to study gene functional associations. The software and description are available at http://www.msu.edu/~jinchen/InteGO.
|State||Published - 2014|
Bibliographical noteFunding Information:
This project has been funded by the U.S. Department of Energy (Chemical Sciences, Geosciences and Biosciences Division, grant no. DE-FG02-91ER20021 to J.C; the National High Technology Research and Development Program of China grant (no. 2012AA020404, 2012AA02A602 and 2012AA02A604) and the National Natural Science Foundation of China grant (no. 61173085) to Y. W.
© 2014 Peng et al.; licensee BioMed Central Ltd.
ASJC Scopus subject areas
- Structural Biology
- Molecular Biology
- Computer Science Applications
- Applied Mathematics