AUTHOR=Lin Yong , Ma Xiaoke 

TITLE=Predicting lincRNA-Disease Association in Heterogeneous Networks Using Co-regularized Non-negative Matrix Factorization

JOURNAL=Frontiers in Genetics

VOLUME=Volume 11 - 2020

YEAR=2021

URL=https://www.frontiersin.org/journals/genetics/articles/10.3389/fgene.2020.622234

DOI=10.3389/fgene.2020.622234

ISSN=1664-8021

ABSTRACT=  Long intergenic non-coding ribonucleic acids (lincRNAs) are critical regulators for many complex diseases, and identification of disease-lincRNA association is finance- and time-consuming. Therefore,  it is necessary to design computational approaches for predicting the disease-lincRNA associations that shed light on revealing the mechanisms of diseases. In this study, we develop a co-regularized nonnegative matrix factorization (aka \emph{Cr-NMF}) to identify potential disease-lincRNA associations by integrating 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 by using regularization. Furthermore, the regularization does not only preserves the topological structure of the lincRNA co-expression network, but also maintains the links 'lincRNA $\rightarrow$ gene $\rightarrow$ 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.