Journal
BRIEFINGS IN BIOINFORMATICS
Volume 22, Issue 4, Pages -Publisher
OXFORD UNIV PRESS
DOI: 10.1093/bib/bbaa350
Keywords
predicting ncRNAs-disease association; data integration; similarity calculation; matrix completion; machine learning; deep learning
Funding
- National Natural Science Foundation of China [61972451, 61672334, 61902230]
- Fundamental Research Funds for the Central Universities, Shaanxi Normal University [GK201901010, 2018TS079]
Ask authors/readers for more resources
The study systematically introduces three common non-coding RNAs, namely miRNAs, lncRNAs, and circRNAs, as well as the computational methods for predicting their association with diseases. These methods include database introduction, RNA and disease similarity calculations, and classification of ncRNA-disease prediction methods into five types. Computational methods can be used to predict and study the relationships between these non-coding RNAs and diseases.
The studies on relationships between non-coding RNAs and diseases are widely carried out in recent years. A large number of experimental methods and technologies of producing biological data have also been developed. However, due to their high labor cost and production time, nowadays, calculation-based methods, especially machine learning and deep learning methods, have received a lot of attention and been used commonly to solve these problems. From a computational point of view, this survey mainly introduces three common non-coding RNAs, i.e. miRNAs, lncRNAs and circRNAs, and the related computational methods for predicting their association with diseases. First, the mainstream databases of above three non-coding RNAs are introduced in detail. Then, we present several methods for RNA similarity and disease similarity calculations. Later, we investigate ncRNA-disease prediction methods in details and classify these methods into five types: network propagating, recommend system, matrix completion, machine learning and deep learning. Furthermore, we provide a summary of the applications of these five types of computational methods in predicting the associations between diseases and miRNAs, lncRNAs and circRNAs, respectively. Finally, the advantages and limitations of various methods are identified, and future researches and challenges are also discussed.
Authors
I am an author on this paper
Click your name to claim this paper and add it to your profile.
Reviews
Recommended
No Data Available