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CausalR: extracting mechanistic sense from genome scale data

SUMMARY: Utilization of causal interaction data enables mechanistic rather than descriptive interpretation of genome-scale data. Here we present CausalR, the first open source causal network analysis platform. Implemented functions enable regulator prediction and network reconstruction, with network...

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Detalles Bibliográficos
Autores principales: Bradley, Glyn, Barrett, Steven J
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Oxford University Press 2017
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5870775/
https://www.ncbi.nlm.nih.gov/pubmed/28666369
http://dx.doi.org/10.1093/bioinformatics/btx425
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author Bradley, Glyn
Barrett, Steven J
author_facet Bradley, Glyn
Barrett, Steven J
author_sort Bradley, Glyn
collection PubMed
description SUMMARY: Utilization of causal interaction data enables mechanistic rather than descriptive interpretation of genome-scale data. Here we present CausalR, the first open source causal network analysis platform. Implemented functions enable regulator prediction and network reconstruction, with network and annotation files created for visualization in Cytoscape. False positives are limited using the introduced Sequential Causal Analysis of Networks approach. AVAILABILITY AND IMPLEMENTATION: CausalR is implemented in R, parallelized, and is available from Bioconductor SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
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spelling pubmed-58707752018-03-29 CausalR: extracting mechanistic sense from genome scale data Bradley, Glyn Barrett, Steven J Bioinformatics Applications Notes SUMMARY: Utilization of causal interaction data enables mechanistic rather than descriptive interpretation of genome-scale data. Here we present CausalR, the first open source causal network analysis platform. Implemented functions enable regulator prediction and network reconstruction, with network and annotation files created for visualization in Cytoscape. False positives are limited using the introduced Sequential Causal Analysis of Networks approach. AVAILABILITY AND IMPLEMENTATION: CausalR is implemented in R, parallelized, and is available from Bioconductor SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online. Oxford University Press 2017-11-15 2017-06-29 /pmc/articles/PMC5870775/ /pubmed/28666369 http://dx.doi.org/10.1093/bioinformatics/btx425 Text en © The Author 2017. Published by Oxford University Press. http://creativecommons.org/licenses/by/4.0/ This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Applications Notes
Bradley, Glyn
Barrett, Steven J
CausalR: extracting mechanistic sense from genome scale data
title CausalR: extracting mechanistic sense from genome scale data
title_full CausalR: extracting mechanistic sense from genome scale data
title_fullStr CausalR: extracting mechanistic sense from genome scale data
title_full_unstemmed CausalR: extracting mechanistic sense from genome scale data
title_short CausalR: extracting mechanistic sense from genome scale data
title_sort causalr: extracting mechanistic sense from genome scale data
topic Applications Notes
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5870775/
https://www.ncbi.nlm.nih.gov/pubmed/28666369
http://dx.doi.org/10.1093/bioinformatics/btx425
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