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Robust de novo pathway enrichment with KeyPathwayMiner 5

Identifying functional modules or novel active pathways, recently termed de novo pathway enrichment, is a computational systems biology challenge that has gained much attention during the last decade. Given a large biological interaction network, KeyPathwayMiner extracts connected subnetworks that a...

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Detalles Bibliográficos
Autores principales: Alcaraz, Nicolas, List, Markus, Dissing-Hansen, Martin, Rehmsmeier, Marc, Tan, Qihua, Mollenhauer, Jan, Ditzel, Henrik J., Baumbach, Jan
Formato: Online Artículo Texto
Lenguaje:English
Publicado: F1000Research 2016
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4965696/
https://www.ncbi.nlm.nih.gov/pubmed/27540470
http://dx.doi.org/10.12688/f1000research.9054.1
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author Alcaraz, Nicolas
List, Markus
Dissing-Hansen, Martin
Rehmsmeier, Marc
Tan, Qihua
Mollenhauer, Jan
Ditzel, Henrik J.
Baumbach, Jan
author_facet Alcaraz, Nicolas
List, Markus
Dissing-Hansen, Martin
Rehmsmeier, Marc
Tan, Qihua
Mollenhauer, Jan
Ditzel, Henrik J.
Baumbach, Jan
author_sort Alcaraz, Nicolas
collection PubMed
description Identifying functional modules or novel active pathways, recently termed de novo pathway enrichment, is a computational systems biology challenge that has gained much attention during the last decade. Given a large biological interaction network, KeyPathwayMiner extracts connected subnetworks that are enriched for differentially active entities from a series of molecular profiles encoded as binary indicator matrices. Since interaction networks constantly evolve, an important question is how robust the extracted results are when the network is modified. We enable users to study this effect through several network perturbation techniques and over a range of perturbation degrees. In addition, users may now provide a gold-standard set to determine how enriched extracted pathways are with relevant genes compared to randomized versions of the original network.
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spelling pubmed-49656962016-08-17 Robust de novo pathway enrichment with KeyPathwayMiner 5 Alcaraz, Nicolas List, Markus Dissing-Hansen, Martin Rehmsmeier, Marc Tan, Qihua Mollenhauer, Jan Ditzel, Henrik J. Baumbach, Jan F1000Res Software Tool Article Identifying functional modules or novel active pathways, recently termed de novo pathway enrichment, is a computational systems biology challenge that has gained much attention during the last decade. Given a large biological interaction network, KeyPathwayMiner extracts connected subnetworks that are enriched for differentially active entities from a series of molecular profiles encoded as binary indicator matrices. Since interaction networks constantly evolve, an important question is how robust the extracted results are when the network is modified. We enable users to study this effect through several network perturbation techniques and over a range of perturbation degrees. In addition, users may now provide a gold-standard set to determine how enriched extracted pathways are with relevant genes compared to randomized versions of the original network. F1000Research 2016-06-28 /pmc/articles/PMC4965696/ /pubmed/27540470 http://dx.doi.org/10.12688/f1000research.9054.1 Text en Copyright: © 2016 Alcaraz N et al. http://creativecommons.org/licenses/by/4.0/ This is an open access article distributed under the terms of the Creative Commons Attribution Licence, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Software Tool Article
Alcaraz, Nicolas
List, Markus
Dissing-Hansen, Martin
Rehmsmeier, Marc
Tan, Qihua
Mollenhauer, Jan
Ditzel, Henrik J.
Baumbach, Jan
Robust de novo pathway enrichment with KeyPathwayMiner 5
title Robust de novo pathway enrichment with KeyPathwayMiner 5
title_full Robust de novo pathway enrichment with KeyPathwayMiner 5
title_fullStr Robust de novo pathway enrichment with KeyPathwayMiner 5
title_full_unstemmed Robust de novo pathway enrichment with KeyPathwayMiner 5
title_short Robust de novo pathway enrichment with KeyPathwayMiner 5
title_sort robust de novo pathway enrichment with keypathwayminer 5
topic Software Tool Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4965696/
https://www.ncbi.nlm.nih.gov/pubmed/27540470
http://dx.doi.org/10.12688/f1000research.9054.1
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