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Protein Networks

Protein Networks

An organism can have thousands of different proteins, and these proteins must cooperate to ensure the health of an organism. Proteins bind to other proteins and form complexes to carry out their functions. Many proteins interact with multiple other proteins creating a complex network of protein interactions.
These interactions can be represented through maps depicting protein-protein interaction networks, represented as nodes and edges. Nodes are circles that are representative of a protein,...

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Updated: Jun 6, 2026

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

Identification of aberrant pathway and network activity from high-throughput data.

M F Ochs¹, R Karchin, H Ressom

¹Departments of Oncology and Health Science Informatics, Johns Hopkins University, Baltimore, MD 19075, USA. mfo@jhu.edu

Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing

|December 2, 2010

Summary

Understanding cellular pathways and networks is crucial for disease research. Analyzing high-throughput data helps identify coordinated biological activity changes driving phenotypes, improving disease understanding and therapeutic targeting.

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Last Updated: Jun 6, 2026

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

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Using Human Differentially Expressed Gene Lists to Perform Downstream Pathway Enrichment Analysis and Target Prioritization

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Published on: October 3, 2025

Area of Science:

Systems biology
Molecular biology
Genomics

Background:

Cancer and metabolic diseases are driven by coordinated changes in cellular pathways and networks, not just single gene mutations.
Gene-focused analyses often fail to capture the full spectrum of molecular changes underlying complex diseases.
Understanding biological activity in cellular pathways is essential for disease etiology.

Framework:

The workshop explored methods to infer pathway and network alterations from high-throughput data.
Focus on analyzing coordinated changes in biological activity that influence cellular phenotype.
Developing tools for pathway and network analysis is key.

Implementation:

High-throughput data analysis techniques were discussed.
Approaches to link pathway changes to observable phenotypes were examined.
The need for robust computational tools was highlighted.

Implications:

Improved understanding of complex diseases like cancer and metabolic disorders.
Enhanced ability to identify critical molecular targets for therapeutic intervention.
Advancement of precision medicine through pathway-centric approaches.