Affiliation:
1. Division of Biomedical Engineering at the University of Saskatchewan
2. School of Computer Science at Shaanxi Normal University
3. School of Mathematics and Statistics at Hainan Normal University, Haikou, China
4. College of Engineering and the Department of Computer Science at University of Saskatchewan
Abstract
Abstract
Biomolecules, such as microRNAs, circRNAs, lncRNAs and genes, are functionally interdependent in human cells, and all play critical roles in diverse fundamental and vital biological processes. The dysregulations of such biomolecules can cause diseases. Identifying the associations between biomolecules and diseases can uncover the mechanisms of complex diseases, which is conducive to their diagnosis, treatment, prognosis and prevention. Due to the time consumption and cost of biologically experimental methods, many computational association prediction methods have been proposed in the past few years. In this study, we provide a comprehensive review of machine learning-based approaches for predicting disease–biomolecule associations with multi-view data sources. Firstly, we introduce some databases and general strategies for integrating multi-view data sources in the prediction models. Then we discuss several feature representation methods for machine learning-based prediction models. Thirdly, we comprehensively review machine learning-based prediction approaches in three categories: basic machine learning methods, matrix completion-based methods and deep learning-based methods, while discussing their advantages and disadvantages. Finally, we provide some perspectives for further improving biomolecule–disease prediction methods.
Funder
National Natural Science Foundation of China
China Scholarship Council
Natural Science and Engineering Research Council of Canada
Publisher
Oxford University Press (OUP)
Subject
Genetics,Molecular Biology,Biochemistry,General Medicine
Cited by
13 articles.
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