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Extended notions of sign consistency to relate experimental data to signaling and regulatory network topologies

机译:标志一致性的扩展概念将实验数据与信令和监管网络拓扑相关联

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摘要

BackgroundA rapidly growing amount of knowledge about signaling and gene regulatory networks is available in databases such as KEGG, Reactome, or RegulonDB. There is an increasing need to relate this knowledge to high-throughput data in order to (in)validate network topologies or to decide which interactions are present or inactive in a given cell type under a particular environmental condition. Interaction graphs provide a suitable representation of cellular networks with information flows and methods based on sign consistency approaches have been shown to be valuable tools to (i) predict qualitative responses, (ii) to test the consistency of network topologies and experimental data, and (iii) to apply repair operations to the network model suggesting missing or wrong interactions.
机译:背景技术诸如KEGG,Reactome或RegulonDB等数据库中提供了有关信号和基因调控网络的快速增长的知识。越来越需要将此知识与高通量数据相关联,以(无效)网络拓扑或确定在特定环境条件下给定小区类型中存在或不活跃的相互作用。交互作用图提供了具有信息流的蜂窝网络的合适表示,并且基于符号一致性方法的方法已被证明是有价值的工具,可用于(i)预测定性响应,(ii)测试网络拓扑和实验数据的一致性,以及( iii)将修复操作应用于暗示缺少或错误交互的网络模型。

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