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• W3119582011 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 Cr-NMF ) 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." @default.
• W3119582011 created "2021-01-18" @default.
• W3119582011 creator A5029088205 @default.
• W3119582011 creator A5056923469 @default.
• W3119582011 date "2021-01-12" @default.
• W3119582011 modified "2023-09-29" @default.
• W3119582011 title "Predicting lincRNA-Disease Association in Heterogeneous Networks Using Co-regularized Non-negative Matrix Factorization" @default.
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• W3119582011 doi "https://doi.org/10.3389/fgene.2020.622234" @default.
• W3119582011 hasPubMedCentralId "https://www.ncbi.nlm.nih.gov/pmc/articles/7835800" @default.
• W3119582011 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/33510774" @default.
• W3119582011 hasPublicationYear "2021" @default.
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