Journal
JOURNAL OF THEORETICAL BIOLOGY
Volume 401, Issue -, Pages 30-37Publisher
ACADEMIC PRESS LTD- ELSEVIER SCIENCE LTD
DOI: 10.1016/j.jtbi.2016.04.020
Keywords
Protein-protein interaction; Gene Ontology; Ascending similarity; Descending similarity; Feature integration; Support vector machine
Categories
Funding
- Sciences Foundation of Guangzhou Maritime Institute [K31012B09]
- Foundation for innovation project in Higher Education of Guangdong, China [A510602, B510620]
- Science and Technology Research and Development Program of Guangzhou [201510010238]
- Sciences and Technology Project of Guangdong Province Transportation Hall, China [2012-02-045]
Ask authors/readers for more resources
Identifying protein-protein interactions is important in molecular biology. Experimental methods to this issue have their limitations, and computational approaches have attracted more and more attentions from the biological community. The semantic similarity derived from the Gene Ontology (GO) annotation has been regarded as one of the most powerful indicators for protein interaction. However, conventional methods based on GO similarity fail to take advantage of the specificity of GO terms in the ontology graph. We proposed a GO-based method to predict protein-protein interaction by integrating different kinds of similarity measures derived from the intrinsic structure of GO graph. We extended five existing methods to derive the semantic similarity measures from the descending part of two GO terms in the GO graph, then adopted a feature integration strategy to combines both the ascending and the descending similarity scores derived from the three sub-ontologies to construct various kinds of features to characterize each protein pair. Support vector machines (SVM) were employed as discriminate classifiers, and five-fold cross validation experiments were conducted on both human and yeast protein-protein interaction datasets to evaluate the performance of different kinds of integrated features, the experimental results suggest the best performance of the feature that combines information from both the ascending and the descending parts of the three ontologies. Our method is appealing for effective prediction of protein-protein interaction. (C) 2016 Elsevier Ltd. All rights reserved.
Authors
I am an author on this paper
Click your name to claim this paper and add it to your profile.
Reviews
Recommended
No Data Available