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Genomics

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Genomics is the science of genomes: it is the study of all the genetic material of an organism. In humans, the genome consists of information carried in 23 pairs of chromosomes in the nucleus, as well as mitochondrial DNA. In genomics, both coding and non-coding DNA is sequenced and analyzed. Genomics allows a better understanding of all living things, their evolution, and their diversity. It has a myriad of uses: for example, to build phylogenetic trees, to improve productivity and...

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Updated: Nov 10, 2025

Author Spotlight: Integrated Multi-Omics Analysis for Unveiling Multicellular Immune Signatures in Clinical Heart Attack Cohorts

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Using machine learning approaches for multi-omics data analysis: A review.

Parminder S Reel¹, Smarti Reel¹, Ewan Pearson¹

¹Division of Population Health and Genomics, School of Medicine, University of Dundee, Dundee, United Kingdom.

Biotechnology Advances

|April 1, 2021

Summary

This summary is machine-generated.

Integrating multi-omics data using machine learning reveals novel biomarkers for disease prediction and precision medicine. This approach enhances understanding of biological systems in health and disease.

Keywords:

Machine Learning Multi-omics Predictive Modelling Supervised Learning Systems Biology Unsupervised Learning

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Last Updated: Nov 10, 2025

Author Spotlight: Integrated Multi-Omics Analysis for Unveiling Multicellular Immune Signatures in Clinical Heart Attack Cohorts

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Published on: September 20, 2024

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Published on: December 15, 2023

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Published on: July 22, 2025

Area of Science:

Biomedical data science
Systems biology
Computational biology

Background:

High-throughput omic measurement platforms generate vast datasets.
Integrating diverse omics data (genetics, proteomics, metabolomics) is crucial for understanding complex biological systems.
Traditional analysis methods struggle to fully utilize multi-omics data.

Purpose of the Study:

To explore integrative machine learning methods for multi-omics data analysis.
To understand biological systems in both normal physiological functioning and disease states.
To identify novel biomarkers for disease prediction and precision medicine.

Main Methods:

Review of different integrative machine learning algorithms applied to multi-omics data.
Focus on predictive modeling for biological insights.
Discussion of techniques for combining data from genetics, proteomics, and metabolomics.

Main Results:

Machine learning enables effective integration and analysis of multi-omics data.
Identification of new biomarkers with potential for disease prediction and patient stratification.
Enhanced understanding of biological system dynamics through integrated data analysis.

Conclusions:

Integrative machine learning is essential for unlocking the full potential of multi-omics data.
Biomarkers discovered through these methods can advance precision medicine.
Recommendations are provided for interdisciplinary professionals utilizing machine learning in multi-omics research.