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

• Main Authors: Congcong Yan, Zicheng Zhang, Siqi Bao, Ping Hou, Meng Zhou, Chongyong Xu, Jie Sun
• Format: Article
• Language: English
• Published: Elsevier 2020-09-01
• Series: Molecular Therapy: Nucleic Acids
• Subjects: long non-coding RNAs; disease; lncRNA-disease association; computational method; bioinformatics
• Online Access: http://www.sciencedirect.com/science/article/pii/S216225312030144X

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.