Towards integrative gene functional similarity measurement

BMC Bioinformatics

Proceedings

Jin Chen¹  Yadong Wang²  Jiajie Peng³ 

[1] MSU-DOE Plant Research Laboratory, Michigan State University, 48824, East Lansing, MI, USA;Department of Computer Science and Engineering, Michigan State University, 48824, East Lansing, MI, USA;School of Computer Science and Technology, Harbin Institute of Technology, Harbin, China;School of Computer Science and Technology, Harbin Institute of Technology, Harbin, China;MSU-DOE Plant Research Laboratory, Michigan State University, 48824, East Lansing, MI, USA;

关键词: Gene ontology;    Semantic similarity;    Integrative measure;   

DOI  :  10.1186/1471-2105-15-S2-S5

来源: Springer

【 摘 要 】

BackgroundIn 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.ResultsWe 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).ConclusionsInteGO is a systematic and novel method to study gene functional associations. The software and description are available at http://www.msu.edu/~jinchen/InteGO.

【 授权许可】

Unknown   
© Peng et al.; licensee BioMed Central Ltd. 2014. This article is published under license to BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.