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Updated: Mar 8, 2026

Mapping Dysfunctional Protein-Protein Interactions in Disease

Mapping Dysfunctional Protein-Protein Interactions in Disease

Published on: October 24, 2025

Functional Interaction Network Construction and Analysis for Disease Discovery.

Guanming Wu^1,2, Robin Haw³

¹Informatics and Biocomputing Program, Ontario Institute for Cancer Research, 661 University Avenue, Toronto, ON, Canada, M5G 0A3. guanmingwu@gmail.com.

Methods in Molecular Biology (Clifton, N.J.)

|February 3, 2017

Summary

This summary is machine-generated.

We developed a protein functional interaction network and an app, ReactomeFIViz, for analyzing genomic data. This tool enhances biological network analysis for high-throughput experiments.

Keywords:

Biological network Biological pathway Cytoscape Functional interaction Java MySQL Naïve Bayesian Classifier Network-based analysis Reactome ReactomeFIViz

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Area of Science:

Bioinformatics
Systems Biology
Computational Biology

Background:

Network-based approaches offer holistic analysis of genomic data from high-throughput experiments.
Biological pathway knowledgebases like Reactome are crucial for understanding gene and protein functions.
Existing tools may lack comprehensive coverage or user-friendly interfaces for network analysis.

Purpose of the Study:

To construct a reliable protein functional interaction network covering a significant portion of human genes.
To develop ReactomeFIViz, an application for Cytoscape, to visualize and analyze biological networks.
To detail the methodology for building a functional interaction network using machine learning and pathway data.

Main Methods:

Integration of multiple external data sources and human-curated pathway databases.
Development of a Naïve Bayesian Classifier for predicting protein interactions.
Construction of a functional interaction database and the ReactomeFIViz application for Cytoscape.

Main Results:

A comprehensive protein functional interaction network encompassing approximately 60% of human genes.
The ReactomeFIViz app enables network-based analysis of gene lists within the Cytoscape platform.
Detailed procedures for network construction and data analysis are provided.

Conclusions:

The developed network and ReactomeFIViz tool provide a powerful platform for genomic data analysis.
Network-based approaches, leveraging curated pathways and machine learning, enhance biological insights.
This work facilitates the exploration of complex biological systems through integrated network analysis.