Data resources and computational methods for lncRNA-disease association prediction

Nan Sheng, Lan Huang*, Yuting Lu, Hao Wang, Lili Yang, Ling Gao, Xuping Xie, Yuan Fu, Yan Wang

*Corresponding author for this work

Research output: Contribution to journalReview Articlepeer-review

3 Citations (SciVal)
156 Downloads (Pure)


Increasing interest has been attracted in deciphering the potential disease pathogenesis through lncRNA-disease association (LDA) prediction, regarding to the diverse functional roles of lncRNAs in genome regulation. Whilst, computational models and algorithms benefit systematic biology research, even facilitate the classical biological experimental procedures. In this review, we introduce representative diseases associated with lncRNAs, such as cancers, cardiovascular diseases, and neurological diseases. Current publicly available resources related to lncRNAs and diseases have also been included. Furthermore, all of the 64 computational methods for LDA prediction have been divided into 5 groups, including machine learning-based methods, network propagation-based methods, matrix factorization- and completion-based methods, deep learning-based methods, and graph neural network-based methods. The common evaluation methods and metrics in LDA prediction have also been discussed. Finally, the challenges and future trends in LDA prediction have been discussed. Recent advances in LDA prediction approaches have been summarized in the GitHub repository at

Original languageEnglish
Article number106527
Number of pages18
JournalComputers in Biology and Medicine
Early online date05 Jan 2023
Publication statusPublished - 01 Feb 2023


  • Computational methods
  • Data resources
  • LncRNA-disease association prediction
  • Long non-coding RNAs
  • Neural Networks, Computer
  • RNA, Long Noncoding/genetics
  • Algorithms
  • Humans
  • Computational Biology/methods
  • Neoplasms/genetics


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