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-aid inference of disease-associated RNAs has become a promising avenue for facilitating the study of lncRNA functions and provided complementary value for experimental studies. In this study, we first summarized data and knowledge resources publicly available for the lncRNA-disease association study. Then we presented an updated systematic overview of dozens of computational methods and models for inferring lncRNA-disease association proposed in recent years. Finally, we explored the perspectives and challenges for further studies. Our study provides a little guide for biologists and medical scientists to look for dedicated resources and more competent tools for accelerating the unraveling of disease-associated lncRNAs.