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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
Descripción
Sumario: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.