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Related Experiment Video

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Author Spotlight: Enhancing PSC-to-Functional Cell Differentiation Using ML Models Based on Live-Cell Bright-Field Imaging

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Learning Differential Module Networks Across Multiple Experimental Conditions.

Pau Erola¹, Eric Bonnet², Tom Michoel^3,4

¹Division of Genetics and Genomics, Roslin Institute, University of Edinburgh, Midlothian, Scotland, UK.

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

|December 15, 2018

Summary

This summary is machine-generated.

Module network inference reconstructs gene regulatory networks using statistical methods. This study reviews the theory and demonstrates Lemon-Tree software for gene expression analysis and differential network learning.

Keywords:

Bayesian analysis Differential networks Gene regulatory network inference Module networks

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Area of Science:

Computational Biology
Systems Biology
Bioinformatics

Background:

Gene regulatory networks (GRNs) control cellular functions.
Module network inference is a statistical approach to infer GRNs.
Probabilistic graphical models are used to identify coregulated gene modules and their regulatory programs.

Purpose of the Study:

To review the fundamental theory of module network inference.
To present protocols for gene regulatory network reconstruction using Lemon-Tree software.
To demonstrate the application of Lemon-Tree for learning differential module networks from human gene expression data.

Main Methods:

Module network inference utilizing probabilistic graphical models.
Application of Lemon-Tree software for GRN reconstruction.
Analysis of genome-wide gene expression and other omics data.
Learning differential module networks across multiple experimental conditions.

Main Results:

The study provides a theoretical overview of module network inference.
Protocols for common GRN reconstruction scenarios are presented.
Lemon-Tree software is shown to be effective for analyzing human gene expression data.
The capability of Lemon-Tree to infer differential module networks is demonstrated.

Conclusions:

Module network inference is a powerful statistical method for understanding gene regulation.
Lemon-Tree software offers a practical tool for GRN reconstruction and differential network analysis.
The findings facilitate the study of gene regulatory programs in various biological contexts.