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Updated: Jul 24, 2025

Inherent Dynamics Visualizer, an Interactive Application for Evaluating and Visualizing Outputs from a Gene Regulatory Network Inference Pipeline

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AMEND: active module identification using experimental data and network diffusion.

Samuel S Boyd^1,2, Chad Slawson^3,2,4, Jeffrey A Thompson^5,6

¹Department of Biostatistics and Data Science, University of Kansas Medical Center, 3901 Rainbow Blvd., Kansas City, KS, 66103, USA.

BMC Bioinformatics

|July 6, 2023

Summary

This summary is machine-generated.

We developed AMEND, a new algorithm that identifies key gene subnetworks from molecular interaction data. AMEND effectively finds functionally related gene groups, aiding in understanding complex biological experiments.

Keywords:

Module identification Network analysis Omics

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Area of Science:

Bioinformatics
Systems Biology
Computational Biology

Background:

Molecular interaction networks provide context for omics experiments, aiding in understanding gene expression relationships.
Identifying specific gene subsets within networks that explain experimental conditions is a key challenge.
The equivalent change index (ECI) measures gene regulation similarity between experiments.

Purpose of the Study:

To develop a novel algorithm for identifying functionally relevant, connected gene subnetworks.
To leverage the equivalent change index (ECI) and network analysis for biological insight.
To find gene subsets that best represent underlying biological mechanisms in experimental data.

Main Methods:

Developed the Active Module identification using Experimental data and Network Diffusion (AMEND) algorithm.
Employed random walk with restart for gene weighting within protein-protein interaction (PPI) networks.
Utilized a heuristic approach for the Maximum-weight Connected Subgraph problem to find optimal subnetworks.

Main Results:

AMEND successfully identified connected subnetworks with high experimental relevance.
The algorithm returned subnetworks with the largest median ECI by magnitude.
AMEND captured distinct yet related functional gene groups, demonstrating its effectiveness.

Conclusions:

AMEND is an effective, fast, and user-friendly method for network-based active module identification.
The algorithm successfully identifies biologically relevant gene subnetworks.
Freely available code facilitates the application of AMEND in biological research.