Multiview Consensus Graph Learning for lncRNA–Disease Association Prediction

Frontiers in Genetics

Jiawei Luo¹  Qiu Xiao²  Pingjian Ding³  Guanghui Li⁴  Haojiang Tan⁵  Quanmeng Sun⁵  Cheng Liang⁵ 

[1] College of Computer Science and Electronic Engineering, Hunan University, Changsha, China;College of Information Science and Engineering, Hunan Normal University, Changsha, China;School of Computer Science, University of South China, Hengyang, China;School of Information Engineering, East China Jiaotong University, Nanchang, China;School of Information Science and Engineering, Shandong Normal University, Jinan, China;

关键词: lncRNA–disease association;    multiple similarity matrices;    consensus graph learning;    multi-label learning;    survival analysis;   

DOI  :  10.3389/fgene.2020.00089

来源: DOAJ

【 摘 要 】

Long noncoding RNAs (lncRNAs) are a class of noncoding RNA molecules longer than 200 nucleotides. Recent studies have uncovered their functional roles in diverse cellular processes and tumorigenesis. Therefore, identifying novel disease-related lncRNAs might deepen our understanding of disease etiology. However, due to the relatively small number of verified associations between lncRNAs and diseases, it remains a challenging task to reliably and effectively predict the associated lncRNAs for given diseases. In this paper, we propose a novel multiview consensus graph learning method to infer potential disease-related lncRNAs. Specifically, we first construct a set of similarity matrices for lncRNAs and diseases by taking advantage of the known associations. We then iteratively learn a consensus graph from the multiple input matrices and simultaneously optimize the predicted association probability based on a multi-label learning framework. To convey the utility of our method, three state-of-the-art methods are compared with our method on three widely used datasets. The experiment results illustrate that our method could obtain the best prediction performance under different cross validation schemes. The case study analysis implemented for uterine cervical neoplasms further confirmed the utility of our method in identifying lncRNAs as potential prognostic biomarkers in practice.

【 授权许可】

Unknown