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JUMPn: A Streamlined Application for Protein Co-Expression Clustering and Network Analysis in Proteomics

JUMPn: A Streamlined Application for Protein Co-Expression Clustering and Network Analysis in Proteomics

Published on: October 19, 2021

Computational approaches for network-based integrative multi-omics analysis.

Francis E Agamah^1,2, Jumamurat R Bayjanov³, Anna Niehues³

¹Division of Human Genetics, Department of Pathology, Institute of Infectious Disease and Molecular Medicine, Faculty of Health Sciences, University of Cape Town, Cape Town, South Africa.

Frontiers in Molecular Biosciences

|December 1, 2022

Summary

This summary is machine-generated.

Network-based approaches integrate multi-omics data for a holistic view of biological systems. This review classifies these methods, highlighting their use in identifying disease mechanisms and biomarkers, with applications in COVID-19 research.

Keywords:

data integration machine learning multi-modal network multi-omics network causal inference network diffusion/propagation

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Area of Science:

Systems biology
Bioinformatics
Genomics and Molecular Biology

Background:

Omics technologies enable holistic biological system studies.
Integrative data analysis is crucial for understanding cellular processes.
Network-based approaches provide a framework for multi-omics data integration.

Purpose of the Study:

To review network-based multi-omics/multi-modal integrative analytical approaches.
To classify these approaches based on data types, methods, and capabilities.
To provide insights into applications and challenges of multi-omics network integration.

Main Methods:

Classification of network-based multi-omics approaches.
Analysis of node and edge weighting components.
Evaluation of methods for identifying key nodes and subnetworks.

Main Results:

Network-based approaches revolutionize multi-omics analysis by representing interactions in a graph.
These methods can identify biomarkers, disease subtypes, crosstalk, causality, and molecular drivers.
The review provides insights into suitable methods and tools, using COVID-19 as a case study.

Conclusions:

Network-based integration is powerful for understanding complex biological mechanisms.
Challenges include reproducibility, heterogeneity, and interpretability.
Future directions focus on advancing network-based integration methods.