Recent advances on the machine learning methods in predicting ncRNA-protein interactions

• Published in: Molecular genetics and genomics : MGG Vol. 296; no. 2; pp. 243 - 258
• Main Authors: Zhong, Lin, Zhen, Meiqin, Sun, Jianqiang, Zhao, Qi
• Format: Journal Article
• Language: English
• Published: Berlin/Heidelberg Springer Berlin Heidelberg 01.03.2021; Springer Nature B.V
• Subjects: Animal Genetics and Genomics; artificial intelligence; Biochemistry; Bioinformatics; Biomedical and Life Sciences; carcinogenesis; chromosomes; Computational Biology - methods; Computer applications; Epigenetics; Gene Regulatory Networks; genomics; Human Genetics; Humans; Learning algorithms; Life Sciences; Machine Learning; Microbial Genetics and Genomics; Non-coding RNA; Plant Genetics and Genomics; Prediction models; Protein interaction; Proteins; Review; Ribonucleic acid; RNA; RNA, Long Noncoding - metabolism; RNA-binding protein; RNA-Binding Proteins - metabolism; Transcription; transcription (genetics); Transcriptomics; Tumorigenesis; Predictive models; ncRNA-protein interaction; ncRNA; Machine learning methods; Protein
• ISSN: 1617-4615; 1617-4623; 1617-4623
• DOI: 10.1007/s00438-020-01727-0

Recent transcriptomics and bioinformatics studies have shown that ncRNAs can affect chromosome structure and gene transcription, participate in the epigenetic regulation, and take part in diseases such as tumorigenesis. Biologists have found that most ncRNAs usually work by interacting with the corresponding RNA-binding proteins. Therefore, ncRNA-protein interaction is a very popular study in both the biological and medical fields. However, due to the limitations of manual experiments in the laboratory, machine-learning methods for predicting ncRNA-protein interactions are increasingly favored by the researchers. In this review, we summarize several machine learning predictive models of ncRNA-protein interactions over the past few years, and briefly describe the characteristics of these machine learning models. In order to optimize the performance of machine learning models to better predict ncRNA-protein interactions, we give some promising future computational directions at the end.