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A Knowledge Graph Approach to Elucidate the Role of Organellar Pathways in Disease via Biomedical Reports

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Machine learning approaches for predicting biomolecule-disease associations.

Yulian Ding¹, Xiujuan Lei², Bo Liao³

¹Division of Biomedical Engineering at the University of Saskatchewan.

Briefings in Functional Genomics

|February 8, 2021

Summary

This review explores machine learning methods for predicting disease-biomolecule associations using multi-view data. It covers databases, feature representation, and various machine learning approaches for improved disease understanding and treatment.

Keywords:

biomolecule–disease association deep learning feature representation machine learning multi-view data source non-negative matrix factorization

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Area of Science:

Computational biology
Bioinformatics
Genomics

Background:

Biomolecules (microRNAs, circRNAs, lncRNAs, genes) are vital for cellular processes.
Dysregulation of these biomolecules is linked to various diseases.
Understanding biomolecule-disease associations aids in disease diagnosis, treatment, and prevention.

Purpose of the Study:

To provide a comprehensive review of machine learning-based approaches for predicting disease-biomolecule associations.
To discuss strategies for integrating multi-view data sources.
To analyze different machine learning methods, including basic, matrix completion, and deep learning approaches.

Main Methods:

Review of existing literature on machine learning for disease-biomolecule association prediction.
Discussion of databases and data integration strategies.
Categorization and analysis of prediction methods based on machine learning techniques.

Main Results:

Identified and categorized various machine learning-based prediction models.
Discussed the advantages and disadvantages of different computational approaches.
Highlighted the importance of multi-view data integration for enhanced prediction accuracy.

Conclusions:

Machine learning offers efficient alternatives to experimental methods for predicting disease-biomolecule associations.
Further improvements in prediction methods are needed, particularly in data integration and model development.
This review provides a foundation for future research in computational disease-biomolecule association prediction.