InfAcrOnt: calculating cross-ontology term similarities using information flow by a random walk

Liang Cheng,Jie Sun,Meng Zhou,Yang Hu,Yue Jiang,Jiajie Peng,Hong Ju

doi:10.1186/s12864-017-4338-6

Abstract

BackgroundSince the establishment of the first biomedical ontology Gene Ontology (GO), the number of biomedical ontology has increased dramatically. Nowadays over 300 ontologies have been built including extensively used Disease Ontology (DO) and Human Phenotype Ontology (HPO). Because of the advantage of identifying novel relationships between terms, calculating similarity between ontology terms is one of the major tasks in this research area. Though similarities between terms within each ontology have been studied with in silico methods, term similarities across different ontologies were not investigated as deeply. The latest method took advantage of gene functional interaction network (GFIN) to explore such inter-ontology similarities of terms. However, it only used gene interactions and failed to make full use of the connectivity among gene nodes of the network. In addition, all existent methods are particularly designed for GO and their performances on the extended ontology community remain unknown.ResultsWe proposed a method InfAcrOnt to infer similarities between terms across ontologies utilizing the entire GFIN. InfAcrOnt builds a term-gene-gene network which comprised ontology annotations and GFIN, and acquires similarities between terms across ontologies through modeling the information flow within the network by random walk. In our benchmark experiments on sub-ontologies of GO, InfAcrOnt achieves a high average area under the receiver operating characteristic curve (AUC) (0.9322 and 0.9309) and low standard deviations (1.8746e-6 and 3.0977e-6) in both human and yeast benchmark datasets exhibiting superior performance. Meanwhile, comparisons of InfAcrOnt results and prior knowledge on pair-wise DO-HPO terms and pair-wise DO-GO terms show high correlations.ConclusionsThe experiment results show that InfAcrOnt significantly improves the performance of inferring similarities between terms across ontologies in benchmark set.

Highlights

Since the establishment of the first biomedical ontology Gene Ontology (GO), the number of biomedical ontology has increased dramatically
The similarity score of positive group (PG) and negative group (NG) was calculated to evaluate the performance of existing methods. e.g. The performance of InfAcrOnt should be superior if the similarity score of the PG can be prioritized at the top
80 pairs of biological processes (BP)-molecular function (MF) terms associated with common enzymes based on HumanCyc [40] were obtained for human as PG

Summary

Introduction

Since the establishment of the first biomedical ontology Gene Ontology (GO), the number of biomedical ontology has increased dramatically. The latest method took advantage of gene functional interaction network (GFIN) to explore such inter-ontology similarities of terms. It only used gene interactions and failed to make full use of the connectivity among gene nodes of the network. Over 300 biomedical ontologies have been manually curated [12, 13] These ontologies are established for describing different types of characteristics of molecules, such as participation in biological processes (BP), induction of diseases, and so on. Several methods have been developed to calculate similarities between terms across these sub-ontologies [20,21,22], it remains a challenge to achieve high reliability

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Discussion

Conclusion