@inbook{23226325702f4a0f975ecbc01c5a2f04,
title = "Learning Differential Module Networks Across Multiple Experimental Conditions",
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.",
keywords = "Algorithms, Cluster Analysis, Computational Biology/instrumentation, Datasets as Topic, Gene Expression Profiling/instrumentation, Gene Expression Regulation, Gene Regulatory Networks, Humans, Models, Genetic, Software",
author = "Pau Erola and Eric Bonnet and Tom Michoel",
year = "2019",
doi = "10.1007/978-1-4939-8882-2_13",
language = "English",
volume = "1883",
series = "Methods in Molecular Biology",
publisher = "Humana Press",
pages = "303--321",
booktitle = "Gene Regulatory Networks",
}