Impact Factor 4.151
2017 JCR, Clarivate Analytics 2018

Frontiers journals are at the top of citation and impact metrics

Original Research ARTICLE Provisionally accepted The full-text will be published soon. Notify me

Front. Genet. | doi: 10.3389/fgene.2019.00003

Predicting Gene Ontology Function of Human MicroRNAs by Integrating Multiple Networks

  • 1School of Software, Central South University, China
  • 2School of Software, Central South University, China
  • 3School of Computer and Data Science, Henan University of Urban Construction, China

MicroRNAs (miRNAs) are small endogenous non-coding RNAs and take critical part in many human biological processes. Inferring the functions of miRNAs perhaps is an important strategy for understanding the pathogenesis of disease at the molecular level. In this paper, we propose an integrated model, PmiRGO, to infer the gene ontology (GO) functions of miRNAs by integrating multiple data sources including the expression profiles of miRNAs, the miRNA-target interactions and protein-protein interactions (PPI). PmiRGO starts with building a global network consisting of three networks. Then, it employs DeepWalk to learn latent representations as network features of the global heterogeneous network. Finally, the SVM-based models are applied to label the GO terms of miRNAs. Experimental results show that PmiRGO performs significantly better than the existing state-of-the-art method in terms of Fmax. Case study further demonstrates the feasibility of PmiRGO to annotate the potential functions of miRNAs.

Keywords: miRNA function annotation, miRNA co-expression, global heterogeneous network, Latent representations, multi-classification

Received: 02 Nov 2018; Accepted: 07 Jan 2019.

Edited by:

Quan Zou, University of Electronic Science and Technology of China, China

Reviewed by:

Zizhang Sheng, Columbia University Irving Medical Center, United States
Wuritu Yang, Inner Mongolia University, China
Wenji Ma, Columbia University, United States  

Copyright: © 2019 Deng, Wang and Zhang. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

* Correspondence: Dr. Jingpu Zhang, School of Computer and Data Science, Henan University of Urban Construction, Pingdingshan, China,