Machine learning approaches for predicting biomolecule–disease associations

Author:

Ding Yulian1,Lei Xiujuan2ORCID,Liao Bo3,Wu Fang-Xiang4ORCID

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

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