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Structure learning for gene regulatory networks

Inference of biological network structures is often performed on high-dimensional data, yet is hindered by the limited sample size of high throughput “omics” data typically available. To overcome this challenge, often referred to as the “small n, large p problem,” we exploit known organizing princip...

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Autores principales: Federico, Anthony, Kern, Joseph, Varelas, Xaralabos, Monti, Stefano
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10231840/
https://www.ncbi.nlm.nih.gov/pubmed/37200395
http://dx.doi.org/10.1371/journal.pcbi.1011118
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author Federico, Anthony
Kern, Joseph
Varelas, Xaralabos
Monti, Stefano
author_facet Federico, Anthony
Kern, Joseph
Varelas, Xaralabos
Monti, Stefano
author_sort Federico, Anthony
collection PubMed
description Inference of biological network structures is often performed on high-dimensional data, yet is hindered by the limited sample size of high throughput “omics” data typically available. To overcome this challenge, often referred to as the “small n, large p problem,” we exploit known organizing principles of biological networks that are sparse, modular, and likely share a large portion of their underlying architecture. We present SHINE—Structure Learning for Hierarchical Networks—a framework for defining data-driven structural constraints and incorporating a shared learning paradigm for efficiently learning multiple Markov networks from high-dimensional data at large p/n ratios not previously feasible. We evaluated SHINE on Pan-Cancer data comprising 23 tumor types, and found that learned tumor-specific networks exhibit expected graph properties of real biological networks, recapture previously validated interactions, and recapitulate findings in literature. Application of SHINE to the analysis of subtype-specific breast cancer networks identified key genes and biological processes for tumor maintenance and survival as well as potential therapeutic targets for modulating known breast cancer disease genes.
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spelling pubmed-102318402023-06-01 Structure learning for gene regulatory networks Federico, Anthony Kern, Joseph Varelas, Xaralabos Monti, Stefano PLoS Comput Biol Research Article Inference of biological network structures is often performed on high-dimensional data, yet is hindered by the limited sample size of high throughput “omics” data typically available. To overcome this challenge, often referred to as the “small n, large p problem,” we exploit known organizing principles of biological networks that are sparse, modular, and likely share a large portion of their underlying architecture. We present SHINE—Structure Learning for Hierarchical Networks—a framework for defining data-driven structural constraints and incorporating a shared learning paradigm for efficiently learning multiple Markov networks from high-dimensional data at large p/n ratios not previously feasible. We evaluated SHINE on Pan-Cancer data comprising 23 tumor types, and found that learned tumor-specific networks exhibit expected graph properties of real biological networks, recapture previously validated interactions, and recapitulate findings in literature. Application of SHINE to the analysis of subtype-specific breast cancer networks identified key genes and biological processes for tumor maintenance and survival as well as potential therapeutic targets for modulating known breast cancer disease genes. Public Library of Science 2023-05-18 /pmc/articles/PMC10231840/ /pubmed/37200395 http://dx.doi.org/10.1371/journal.pcbi.1011118 Text en © 2023 Federico et al https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
spellingShingle Research Article
Federico, Anthony
Kern, Joseph
Varelas, Xaralabos
Monti, Stefano
Structure learning for gene regulatory networks
title Structure learning for gene regulatory networks
title_full Structure learning for gene regulatory networks
title_fullStr Structure learning for gene regulatory networks
title_full_unstemmed Structure learning for gene regulatory networks
title_short Structure learning for gene regulatory networks
title_sort structure learning for gene regulatory networks
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10231840/
https://www.ncbi.nlm.nih.gov/pubmed/37200395
http://dx.doi.org/10.1371/journal.pcbi.1011118
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