ncRNAInter: a novel strategy based on graph neural network to discover interactions between lncRNA and miRNA

Author:

Zhang Hanyu12ORCID,Wang Yunxia1,Pan Ziqi1,Sun Xiuna1,Mou Minjie1ORCID,Zhang Bing2,Li Zhaorong2,Li Honglin34,Zhu Feng12ORCID

Affiliation:

1. College of Pharmaceutical Sciences, Zhejiang University , Hangzhou 310058, China

2. Innovation Institute for Artificial Intelligence in Medicine of Zhejiang University, Alibaba-Zhejiang University Joint Research Center of Future Digital Healthcare , Hangzhou 330110, China

3. School of Computer Science and Technology, East China Normal University , Shanghai 200062, China

4. Shanghai Key Laboratory of New Drug Design, East China University of Science and Technology , Shanghai 200237, China

Abstract

Abstract In recent years, many studies have illustrated the significant role that non-coding RNA (ncRNA) plays in biological activities, in which lncRNA, miRNA and especially their interactions have been proved to affect many biological processes. Some in silico methods have been proposed and applied to identify novel lncRNA–miRNA interactions (LMIs), but there are still imperfections in their RNA representation and information extraction approaches, which imply there is still room for further improving their performances. Meanwhile, only a few of them are accessible at present, which limits their practical applications. The construction of a new tool for LMI prediction is thus imperative for the better understanding of their relevant biological mechanisms. This study proposed a novel method, ncRNAInter, for LMI prediction. A comprehensive strategy for RNA representation and an optimized deep learning algorithm of graph neural network were utilized in this study. ncRNAInter was robust and showed better performance of 26.7% higher Matthews correlation coefficient than existing reputable methods for human LMI prediction. In addition, ncRNAInter proved its universal applicability in dealing with LMIs from various species and successfully identified novel LMIs associated with various diseases, which further verified its effectiveness and usability. All source code and datasets are freely available at https://github.com/idrblab/ncRNAInter.

Funder

National Natural Science Foundation of China

Natural Science Foundation of Zhejiang Province

National High-Level Talents Special Support Plan of China

Fundamental Research Fund for Central Universities

Key Research and Development Program of Zhejiang Province

Westlake Laboratory of Life Sciences and Biomedicine

Alibaba-Zhejiang University Joint Research Center of Future Digital Healthcare

Information Technology Center of Zhejiang University

Publisher

Oxford University Press (OUP)

Subject

Molecular Biology,Information Systems

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