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Learning Differential Module Networks Across Multiple Experimental Conditions

Research output: Chapter in Book/Report/Conference proceedingChapter in a book

Original languageEnglish
Title of host publicationGene Regulatory Networks
Pages303-321
Number of pages19
Volume1883
DOIs
DatePublished - 2019

Publication series

NameMethods in Molecular Biology
PublisherHumana Press
ISSN (Print)1064-3745

Abstract

Module network inference is a statistical method to reconstruct gene regulatory networks, which uses probabilistic graphical models to learn modules of coregulated genes and their upstream regulatory programs from genome-wide gene expression and other omics data. Here, we review the basic theory of module network inference, present protocols for common gene regulatory network reconstruction scenarios based on the Lemon-Tree software, and show, using human gene expression data, how the software can also be applied to learn differential module networks across multiple experimental conditions.

    Research areas

  • Algorithms, Cluster Analysis, Computational Biology/instrumentation, Datasets as Topic, Gene Expression Profiling/instrumentation, Gene Expression Regulation, Gene Regulatory Networks, Humans, Models, Genetic, Software

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