LncRNA-Disease Association Prediction Using Two-Side Sparse Self-Representation

Abstract

Evidences increasingly indicate the involvement of long non-coding RNAs (lncRNAs) in various biological processes. As the mutations and abnormalities of lncRNAs are closely related to the progression of complex diseases, the identification of lncRNA-disease associations has become an important step toward the understanding and treatment of diseases. Since only a limited number of lncRNA-disease associations have been validated, an increasing number of computational approaches have been developed for predicting potential lncRNA-disease associations. However, how to predict potential associations precisely through computational approaches remains challenging. In this study, we propose a novel two-side sparse self-representation (TSSR) algorithm for lncRNA-disease association prediction. By learning the self-representations of lncRNAs and diseases from known lncRNA-disease associations adaptively, and leveraging the information provided by known lncRNA-disease associations and the intra-associations among lncRNAs and diseases derived from other existing databases, our model could effectively utilize the estimated representations of lncRNAs and diseases to predict potential lncRNA-disease associations. The experiment results on three real data sets demonstrate that our TSSR outperforms other competing methods significantly. Moreover, to further evaluate the effectiveness of TSSR in predicting potential lncRNAs-disease associations, case studies of Melanoma, Glioblastoma, and Glioma are carried out in this paper. The results demonstrate that TSSR can effectively identify some candidate lncRNAs associated with these three diseases.

Computational Methods and Applications for Identifying Disease-Associated lncRNAs as Potential Biomarkers and Therapeutic Targets

“…and recommendation system ideas was applied in three models to predict potential lncRNAdisease associations, including LDASR, ECLDA, and weighted bagging LightGBM model. [49][50][51] Three methods (CNNLDA, CNNDLP, and GCNLDA) were developed by Xuan et al [52][53][54] to construct the final module through the integration of the convolutional module and attention module. LDAPred, proposed by Xuan et al, 55 introduced the convolutional neural network based on the integration of resource allocation and matrix completion.…”

“…47,48 Also, TSSR exploits learned representation matrices as feature matrices to reconstruct the original matrix. 49…”

DSCMF: prediction of LncRNA-disease associations based on dual sparse collaborative matrix factorization

“…The result proves that this method is effective and has great advantages. Ou-Yang et al proposed a new method for predicting LDAs, called the two-side sparse self-representation method [ 24 ]. The advantage of this approach is that it can adaptively learn the self-characterization of lncRNAs and the self-characterization of diseases, a process based on the known LDAs.…”

Regulatory Network of miRNA, lncRNA, Transcription Factor and Target Immune Response Genes in Bovine Mastitis

“…This heterogeneous class of RNAs can modulate gene expression and protein synthesis at the transcriptional and post-transcriptional levels via complementary base pairing 10 . LncRNAs play key roles in regulating several cellular and developmental processes, including genomic imprinting, DNA methylation, splicing and chromatin modi cation 11,12 . With an abundance of binding sites for miRNA and mRNA, lncRNA can act as ceRNA (competing endogenous RNA) and are signi cant regulatory elements in post-transcriptional gene expression 13,14 .…”