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Integration strategies of multi-omics data for machine learning analysis.

Milan Picard¹, Marie-Pier Scott-Boyer¹, Antoine Bodein¹

¹Molecular Medicine Department, CHU de Québec Research Center, Université Laval, Québec, QC, Canada.

Computational and Structural Biotechnology Journal

|July 21, 2021

Summary

This summary is machine-generated.

This review explores multi-omics data integration strategies, focusing on machine learning applications. Integrating diverse omics data enhances biomarker discovery for complex biological systems.

Keywords:

Deep learning Integration strategy Machine learning Multi-omics Multi-view Network

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Area of Science:

Bioinformatics
Computational Biology
Systems Biology

Background:

High-throughput technologies generate vast amounts of multi-omics data (genomics, epigenomics, transcriptomics, proteomics, metabolomics).
Machine learning algorithms have yielded diagnostic and classification biomarkers from single omic measurements.
Current biomarkers often lack integration of multi-omics data, limiting insights into biological complexity.

Purpose of the Study:

To review and categorize recent multi-omics data integration strategies.
To highlight the role of machine learning in multi-omics integration.
To discuss challenges and opportunities in combining complementary omics data.

Main Methods:

Categorization of integration strategies into five types: early, mixed, intermediate, late, and hierarchical.
Focus on machine learning applications within these integration frameworks.
Literature review of existing multi-omics integration methods.

Main Results:

Five distinct multi-omics data integration strategies have been identified and summarized.
Machine learning plays a crucial role in extracting insights from integrated omics data.
The review highlights the necessity of advanced integration methods for comprehensive biological understanding.

Conclusions:

Effective multi-omics data integration is essential for fully leveraging complex biological datasets.
Machine learning-powered integration strategies offer significant potential for biomarker discovery.
Further development of integration methods is needed to address the complexity of biological systems.