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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...
Autores principales: | , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Oxford University Press
2017
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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. |
format | Online Article Text |
id | pubmed-5870775 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2017 |
publisher | Oxford University Press |
record_format | MEDLINE/PubMed |
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 |
work_keys_str_mv | AT bradleyglyn causalrextractingmechanisticsensefromgenomescaledata AT barrettstevenj causalrextractingmechanisticsensefromgenomescaledata |