Predicting LincRNA-Disease Association in Heterogeneous Networks Using Co-regularized Non-negative Matrix Factorization

Overview

Journal Front Genet

Date 2021 Jan 29

PMID 33510774

Citations 1

Authors

Yong Lin

Xiaoke Ma

Affiliations

Soon will be listed here.

Abstract

Long intergenic non-coding ribonucleic acids (lincRNAs) are critical regulators for many complex diseases, and identification of disease-lincRNA association is both costly and time-consuming. Therefore, it is necessary to design computational approaches to predict the disease-lincRNA associations that shed light on the mechanisms of diseases. In this study, we develop a co-regularized non-negative matrix factorization (aka ) to identify potential disease-lincRNA associations by integrating the gene expression of lincRNAs, genetic interaction network for mRNA genes, gene-lincRNA associations, and disease-gene associations. The Cr-NMF algorithm factorizes the disease-lincRNA associations, while the other associations/interactions are integrated using regularization. Furthermore, the regularization does not only preserve the topological structure of the lincRNA co-expression network, but also maintains the links "lincRNA → gene → disease." Experimental results demonstrate that the proposed algorithm outperforms state-of-the-art methods in terms of accuracy on predicting the disease-lincRNA associations. The model and algorithm provide an effective way to explore disease-lncRNA associations.

Citing Articles

A Survey on Computational Methods for Investigation on ncRNA-Disease Association through the Mode of Action Perspective.

Bang D, Gu J, Park J, Jeong D, Koo B, Yi J Int J Mol Sci. 2022; 23(19).

PMID: 36232792 PMC: 9570358. DOI: 10.3390/ijms231911498.

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