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Related Experiment Video

Updated: Apr 14, 2026

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

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

Published on: December 7, 2021

Addressing false discoveries in network inference.

Tobias Petri¹, Stefan Altmann¹, Ludwig Geistlinger¹

¹Ludwig-Maximilians-Universität München, Institut für Informatik, Munich, Germany and.

Bioinformatics (Oxford, England)

|April 26, 2015

Summary

This summary is machine-generated.

Computational inference of gene regulatory networks is improved by considering known regulator targets. A new method, CoRe, corrects for false discoveries and enables accurate genome-wide network inference in eukaryotes.

Related Experiment Videos

Last Updated: Apr 14, 2026

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

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

Published on: December 7, 2021

Area of Science:

Computational biology
Systems biology
Bioinformatics

Background:

Gene regulatory network inference from expression profiles is challenging due to indirect and spurious effects.
Existing methods can be improved by incorporating known regulator targets (local topology).

Purpose of the Study:

To address the high rate of false discoveries in network inference methods that exploit known regulator targets.
To develop a method for reliable performance estimation and accurate genome-wide network inference.

Main Methods:

Development of a confidence score recalibration method (CoRe).
Evaluation of CoRe's impact on false discovery rates and performance estimation.

Main Results:

Methods exploiting known targets exhibit an unexpectedly high false discovery rate, inflating performance estimates.
CoRe effectively reduces the false discovery rate, enabling reliable performance estimation.
Simpson's paradox can obscure issues in common evaluation setups.

Conclusions:

CoRe significantly enhances network inference, leading to more accurate biological process specificity.
Enables the inference of accurate genome-wide regulatory networks in eukaryotes, including a proposed yeast network with over 22,000 interactions.
Highlights potential impact on machine learning approaches beyond network inference.