Multiview Consensus Graph Learning for lncRNA–Disease Association Prediction

Abstract

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.

Bioinformatics Analysis of Long Non-coding RNA and Related Diseases: An Overview

“…Fan et al ( Fan et al, 2020 ) proposed IDSSIM, a calculation model of lncRNA functional similarity based on improved disease semantic similarity. Tan et al ( Tan et al, 2020 ) proposed a multi-view consensus graph learning model to predict lncRNA–disease association. Wei et al ( Wei et al, 2021 ) proposed a convolutional neural network model fused with multiple biological characteristics to predict lncRNA–disease association.…”

Artificial intelligence predictive system of individual survival rate for lung adenocarcinoma

“…In recent years, artificial intelligence has made great progress in cancer research, diagnosis, prognostic prediction, and treatment. Various algorithms have been used to find the lncRNAs closely related to different diseases, so as to provide valuable biomarkers for clinical diagnosis [5] , [6] , [7] , [8] . Artificial intelligence predictive models based on gene expression data could predict prognosis for different tumors [9] , [10] .…”

An Lncrna-disease Association Prediction Method Based on Nearest Neighbor Matrix Completion Model

“…Xie et al [8] proposed a computational model for weighted lncRNA disease association prediction called WLDAP. tan et al [9] proposed a multi-view for lncRNA disease association prediction Consensus graph learning method. Yuan et al [10] proposed a clustering correlation-based lncRNA-disease association prediction method.…”