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Updated: Jul 6, 2026

Mapping Dysfunctional Protein-Protein Interactions in Disease

Mapping Dysfunctional Protein-Protein Interactions in Disease

Published on: October 24, 2025

Protein networks in disease.

Trey Ideker¹, Roded Sharan

¹Department of Bioengineering, University of California at San Diego, La Jolla, California 92093, USA.

Genome Research

|April 3, 2008

Summary

Protein networks are emerging as powerful tools for understanding disease. This review highlights their use in identifying disease genes, analyzing network properties, and classifying diseases for better treatment strategies.

Area of Science:

Systems Biology
Genomics
Computational Biology

Background:

Protein networks have been instrumental in model organisms for studying molecular evolution, cellular robustness, and protein function.
Recent advancements in mammalian protein interaction measurements have propelled protein networks into disease research.

Purpose of the Study:

To review the promising applications of protein networks in understanding the molecular basis of human diseases.
To explore the utility of protein networks in identifying disease genes, characterizing network properties, and classifying diseases.

Main Methods:

Review of existing literature on protein network applications in disease research.
Analysis of four major areas: disease gene identification, network property studies, subnetwork identification, and network-based classification.

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

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Main Results:

Protein networks are effective for identifying novel disease-associated genes.
Network properties provide insights into disease mechanisms.
Disease-related subnetworks can be identified for targeted study.
Network-based approaches facilitate disease classification.

Conclusions:

Protein networks offer a robust framework for unraveling the molecular underpinnings of various diseases.
Future applications in infectious diseases, personalized medicine, and pharmacology are anticipated with improved data quality and coverage.