IntScore: a web tool for confidence scoring of biological interactions
IntScore: a web tool for confidence scoring of biological interactions
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DOI：
10.1093/nar/gks492
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发表时间：
2012-07-01
影响因子：
14.9
通讯作者：
Herwig, Ralf
中科院分区：
文献类型：
--
作者：
Kamburov, Atanas;Stelzl, Ulrich;Herwig, Ralf
了解细胞中可能发生的所有分子相互作用是详细了解细胞过程的关键。目前可用的相互作用数据，如蛋白质-蛋白质相互作用图，已知包含假阳性，这不可避免地降低了基于网络的推断的准确性。因此，交互置信度评分是在获得交互数据之后以及在基于交互网络的推理方法中使用交互数据之前的关键中间步骤。它能够根据它们在细胞中实际发生的可能性来加权个体相互作用，并可用于过滤假阳性。我们描述了一个Web工具，称为IntScore，它计算用户指定的交互集的置信度得分。IntScore提供了六种基于网络拓扑和注释的置信度评分方法。它还能够使用机器学习方法将通过不同方法计算的分数整合到总分数中。IntScore是用户友好的，并有广泛的文档记录。它可以在http://intscore.molgen.mpg.de上免费获得。
Knowledge of all molecular interactions that potentially take place in the cell is a key for a detailed understanding of cellular processes. Currently available interaction data, such as protein-protein interaction maps, are known to contain false positives that inevitably diminish the accuracy of network-based inferences. Interaction confidence scoring is thus a crucial intermediate step after obtaining interaction data and before using it in an interaction network-based inference approach. It enables to weight individual interactions according to the likelihood that they actually take place in the cell, and can be used to filter out false positives. We describe a web tool called IntScore which calculates confidence scores for user-specified sets of interactions. IntScore provides six network topology- and annotation-based confidence scoring methods. It also enables the integration of scores calculated by the different methods into an aggregate score using machine learning approaches. IntScore is user-friendly and extensively documented. It is freely available at http://intscore.molgen.mpg.de.