Analyzing causal relationships in proteomic profiles using CausalPath.
Analyzing causal relationships in proteomic profiles using CausalPath.
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DOI：
10.1016/j.xpro.2021.100955
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发表时间：
2021-12-17
期刊：
影响因子：
--
通讯作者：
Babur O
中科院分区：
文献类型：
--
作者：
Luna A;Siper MC;Korkut A;Durupinar F;Dogrusoz U;Aslan JE;Sander C;Demir E;Babur O
CausalPath （causalpath.org）根据生物学途径的先验知识评估蛋白质组学测量结果，并推断测量特征变化之间的因果关系，例如整体蛋白质和磷酸化蛋白水平。它使用路径资源来确定可观察到的组学特征之间的潜在因果关系，这被称为先验关系。先前的关系的子集是由蛋白质组谱支持的报告和评估的统计显著性。最终的结果是一个信号网络模型，它解释了在实验数据集中观察到的模式。有关使用和执行本协议的完整详情，请参阅。CausalPath （causalpath.org）是一个免费的开源工具，用于探索蛋白质组学数据分析，重点关注蛋白质组学变化揭示的因果关系。CausalPath （causalpath.org）对蛋白质组学测量结果进行评估，以对照生物学途径的先验知识，推断测量特征变化之间的因果关系，如全局蛋白质和磷酸化蛋白水平。它使用路径资源来确定可观察到的组学特征之间的潜在因果关系，这被称为先验关系。先前的关系的子集是由蛋白质组谱支持的报告和评估的统计显著性。最终的结果是一个信号网络模型，它解释了在实验数据集中观察到的模式。
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. A free open-source tool for exploration of proteomic data Analysis focuses on causal relationships revealed by proteomic changes Network visualization and publication-quality figures of CausalPath results 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.
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影响因子：
4.4
作者：
Babur Ö;Dogrusoz U;Çakır M;Aksoy BA;Schultz N;Sander C;Demir E
通讯作者：
Demir E
DOI：
10.1161/atvbaha.120.314647
发表时间：
2021-03
期刊：
Arteriosclerosis, thrombosis, and vascular biology
影响因子：
--
作者：
Aslan JE
通讯作者：
Aslan JE
影响因子：
14.9
作者：
Hornbeck, Peter V.;Kornhauser, Jon M.;Gnad, Florian
通讯作者：
Gnad, Florian
影响因子：
5.8
作者：
Balci, Hasan;Siper, Metin Can;Dogrusoz, Ugur
通讯作者：
Dogrusoz, Ugur
影响因子：
14.9
作者：
Licata, Luana;Lo Surdo, Prisca;Cesareni, Gianni
通讯作者：
Cesareni, Gianni