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Updated: Mar 15, 2026

Author Spotlight: Emerging Technologies and Advanced Tools for Decoding Metabolomics Data Analysis

Author Spotlight: Emerging Technologies and Advanced Tools for Decoding Metabolomics Data Analysis

Published on: November 10, 2023

NEAT: an efficient network enrichment analysis test.

Mirko Signorelli^1,2, Veronica Vinciotti³, Ernst C Wit⁴

¹Johann Bernoulli Institute, University of Groningen, Nijenborgh 9, Groningen, 9747 AG, Netherlands.

BMC Bioinformatics

|September 7, 2016

Summary

This summary is machine-generated.

We developed NEAT, a new network enrichment analysis test. It is faster and more flexible than existing methods, supporting directed and undirected networks for better biological insights.

Keywords:

Enrichment analysis Gene expression Hypergeometric Network

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Area of Science:

Bioinformatics
Computational Biology
Systems Biology

Background:

Network enrichment analysis integrates gene enrichment with gene network data.
Current methods are limited to undirected networks, can be slow, and rely on normality assumptions.

Purpose of the Study:

To introduce NEAT, a novel network enrichment analysis test.
To address limitations of existing methods, including network directionality and computational efficiency.

Main Methods:

Developed NEAT based on the hypergeometric distribution.
Applied NEAT to analyze gene sets and functional associations in yeast networks.
Compared NEAT's performance against existing resampling-based methods via simulations.

Main Results:

NEAT supports directed, partially directed, and undirected networks.
Simulations show NEAT is significantly faster than alternative methods.
NEAT demonstrates comparable or superior enrichment detection capabilities.

Conclusions:

NEAT offers a flexible and efficient approach to network enrichment analysis.
The NEAT method overcomes limitations of previous resampling-based tests.
The NEAT R package is available for public use on CRAN.