Research Area:  Machine Learning

Abstract:

Measuring the semantic similarity between Gene Ontology (GO) terms is an essential step in functional bioinformatics research. We implemented a software named GOGO for calculating the semantic similarity between GO terms. GOGO has the advantages of both information-content-based and hybrid methods, such as Resnik’s and Wang’s methods. Moreover, GOGO is relatively fast and does not need to calculate information content (IC) from a large gene annotation corpus but still has the advantage of using IC. This is achieved by considering the number of children nodes in the GO directed acyclic graphs when calculating the semantic contribution of an ancestor node giving to its descendent nodes. GOGO can calculate functional similarities between genes and then cluster genes based on their functional similarities. Evaluations performed on multiple pathways retrieved from the saccharomyces genome database (SGD) show that GOGO can accurately and robustly cluster genes based on functional similarities. We release GOGO as a web server and also as a stand-alone tool, which allows convenient execution of the tool for a small number of GO terms or integration of the tool into bioinformatics pipelines for large-scale calculations.

Keywords:  
Semantic Similarity
Gene Ontology Terms
Machine Learning
Deep Learning

Author(s) Name:  Chenguang Zhao & Zheng Wang

Publisher name:  Springer Nature

DOI:  10.1038/s41598-018-33219-y

Volume Information:   volume 8, Article number: 15107 (2018)

Paper Link:   https://www.nature.com/articles/s41598-018-33219-y