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. 2018 Mar 19;12(Suppl 2):18.
doi: 10.1186/s12918-018-0539-0.

Improving the measurement of semantic similarity by combining gene ontology and co-functional network: a random walk based approach

Jiajie Peng  1   2   3 , Xuanshuo Zhang  4 , Weiwei Hui  4 , Junya Lu  4 , Qianqian Li  4 , Shuhui Liu  4 , Xuequn Shang  4   5
Affiliations

Improving the measurement of semantic similarity by combining gene ontology and co-functional network: a random walk based approach

Jiajie Peng et al. BMC Syst Biol. .

Abstract

Background: Gene Ontology (GO) is one of the most popular bioinformatics resources. In the past decade, Gene Ontology-based gene semantic similarity has been effectively used to model gene-to-gene interactions in multiple research areas. However, most existing semantic similarity approaches rely only on GO annotations and structure, or incorporate only local interactions in the co-functional network. This may lead to inaccurate GO-based similarity resulting from the incomplete GO topology structure and gene annotations.

Results: We present NETSIM2, a new network-based method that allows researchers to measure GO-based gene functional similarities by considering the global structure of the co-functional network with a random walk with restart (RWR)-based method, and by selecting the significant term pairs to decrease the noise information. Based on the EC number (Enzyme Commission)-based groups of yeast and Arabidopsis, evaluation test shows that NETSIM2 can enhance the accuracy of Gene Ontology-based gene functional similarity.

Conclusions: Using NETSIM2 as an example, we found that the accuracy of semantic similarities can be significantly improved after effectively incorporating the global gene-to-gene interactions in the co-functional network, especially on the species that gene annotations in GO are far from complete.

Keywords: Gene Ontology; Random walk with restart; Semantic similarity.

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Figures

Fig. 1
Fig. 1
The workflow of NETSIM2
Fig. 2
Fig. 2
Performance comparison of different measures on GO’s molecular function terms in yeast (a) and Arabidopsis (b)
Fig. 3
Fig. 3
Number of ECs for which NETSIM2, NETSIM, Wang and Relevance measures performed the best for yeast (a) and Arabidopsis (b) based on molecular function terms
Fig. 4
Fig. 4
Performance comparison on LFC scores of similarity measures on GO’s biological process in yeast (a) and Arabidopsis (b)
Fig. 5
Fig. 5
Number of ECs for which NETSIM2, NETSIM, Wang and Relevance measures performed the best for yeast (a) and Arabidopsis (b) based on biological process terms
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