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Analyzing causal relationships in proteomic profiles using CausalPath
CausalPath (causalpath.org) evaluates proteomic measurements against prior knowledge of biological pathways and infers causality between changes in measured features, such as global protein and phospho-protein levels. It uses pathway resources to determine potential causality between observable omic...
Autores principales: | , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Elsevier
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8633371/ https://www.ncbi.nlm.nih.gov/pubmed/34877547 http://dx.doi.org/10.1016/j.xpro.2021.100955 |
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author | Luna, Augustin Siper, Metin Can Korkut, Anil Durupinar, Funda Dogrusoz, Ugur Aslan, Joseph E. Sander, Chris Demir, Emek Babur, Ozgun |
author_facet | Luna, Augustin Siper, Metin Can Korkut, Anil Durupinar, Funda Dogrusoz, Ugur Aslan, Joseph E. Sander, Chris Demir, Emek Babur, Ozgun |
author_sort | Luna, Augustin |
collection | PubMed |
description | CausalPath (causalpath.org) evaluates proteomic measurements against prior knowledge of biological pathways and infers causality between changes in measured features, such as global protein and phospho-protein levels. It uses pathway resources to determine potential causality between observable omic features, which are called prior relations. The subset of the prior relations that are supported by the proteomic profiles are reported and evaluated for statistical significance. The end result is a network model of signaling that explains the patterns observed in the experimental dataset. For complete details on the use and execution of this protocol, please refer to Babur et al. (2021). |
format | Online Article Text |
id | pubmed-8633371 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | Elsevier |
record_format | MEDLINE/PubMed |
spelling | pubmed-86333712021-12-06 Analyzing causal relationships in proteomic profiles using CausalPath Luna, Augustin Siper, Metin Can Korkut, Anil Durupinar, Funda Dogrusoz, Ugur Aslan, Joseph E. Sander, Chris Demir, Emek Babur, Ozgun STAR Protoc Protocol CausalPath (causalpath.org) evaluates proteomic measurements against prior knowledge of biological pathways and infers causality between changes in measured features, such as global protein and phospho-protein levels. It uses pathway resources to determine potential causality between observable omic features, which are called prior relations. The subset of the prior relations that are supported by the proteomic profiles are reported and evaluated for statistical significance. The end result is a network model of signaling that explains the patterns observed in the experimental dataset. For complete details on the use and execution of this protocol, please refer to Babur et al. (2021). Elsevier 2021-11-23 /pmc/articles/PMC8633371/ /pubmed/34877547 http://dx.doi.org/10.1016/j.xpro.2021.100955 Text en © 2021 The Authors https://creativecommons.org/licenses/by/4.0/This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Protocol Luna, Augustin Siper, Metin Can Korkut, Anil Durupinar, Funda Dogrusoz, Ugur Aslan, Joseph E. Sander, Chris Demir, Emek Babur, Ozgun Analyzing causal relationships in proteomic profiles using CausalPath |
title | Analyzing causal relationships in proteomic profiles using CausalPath |
title_full | Analyzing causal relationships in proteomic profiles using CausalPath |
title_fullStr | Analyzing causal relationships in proteomic profiles using CausalPath |
title_full_unstemmed | Analyzing causal relationships in proteomic profiles using CausalPath |
title_short | Analyzing causal relationships in proteomic profiles using CausalPath |
title_sort | analyzing causal relationships in proteomic profiles using causalpath |
topic | Protocol |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8633371/ https://www.ncbi.nlm.nih.gov/pubmed/34877547 http://dx.doi.org/10.1016/j.xpro.2021.100955 |
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