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

Abstract

Long non-coding RNAs (lncRNAs) have been recognized as critical components of a broad genomic regulatory network and play pivotal roles in physiological and pathological processes. Identification of disease-associated lncRNAs is becoming increasingly crucial for fundamentally improving our understanding of molecular mechanisms of disease and developing novel biomarkers and therapeutic targets. Considering lower efficiency and higher time and labor cost of biological experiments, computer-aided inference of disease-associated RNAs has become a promising avenue for facilitating the study of lncRNA functions and provides complementary value for experimental studies. In this study, we first summarize data and knowledge resources publicly available for the study of lncRNA-disease associations. Then, we present an updated systematic overview of dozens of computational methods and models for inferring lncRNA-disease associations proposed in recent years. Finally, we explore the perspectives and challenges for further studies. Our study provides a guide for biologists and medical scientists to look for dedicated resources and more competent tools for accelerating the unraveling of disease-associated lncRNAs.

Keywords: bioinformatics; computational method; disease; lncRNA-disease association; long non-coding RNAs.

Graphical abstract

Figure 1

Schematic Workflow of Matrix Completion-Based Methods Three matrices (including the lncRNA-disease association matrix, lncRNA-lncRNA matrix, and disease-disease matrix) were first obtained as the input data. Then, feature extraction was accomplished based on the above three matrices to obtain lncRNA feature vectors and disease feature vectors. Finally, matrix completion methods were performed on the lncRNA-disease association matrix to acquire the lncRNA-disease association.

Figure 2

Schematic Workflow of Resource Allocation-Based Methods Multi-type data source matrices were first obtained as the input data. Then, a heterogeneous multilayer network is constructed, and the edges are weighted by the corresponding values of the matrix. Finally, the lncRNA-disease scoring matrix was produced by post-processing resource allocation on the heterogeneous network.

Figure 3

Schematic Workflow of Recommendation Algorithm-Based Methods Multi-type data source matrices were first obtained as the input data. Then, recommendation matrices at multiple levels (e.g., lncRNAs, miRNAs) are obtained by applying a recommendation system algorithm. Finally, the possibility of the potential relationship between lncRNA and disease is measured through the combination of the recommendation matrices.