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Integrated analysis of multiple data sources reveals modular structure of biological networks

机译:多种数据源的综合分析揭示了生物网络的模块化结构

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It has been a challenging task to integrate high-throughput data into investigations of the systematic and dynamic organization of biological networks. Here, we presented a simple hierarchical clustering algorithm that goes a long way to achieve this aim. Our method effectively reveals the modular structure of the yeast protein-protein interaction network and distinguishes protein complexes from functional modules by integrating high-throughput protein-protein interaction data with the added subcellular localization and expression profile data. Furthermore, we take advantage of the detected modules to provide a reliably functional context for the uncharacterized components within modules. On the other hand, the integration of various protein-protein association information makes our method robust to false-positives, especially for derived protein complexes. More importantly, this simple method can be extended naturally to other types of data fusion and provides a framework for the study of more comprehensive properties of the biological network and other forms of complex networks. (c) 2006 Elsevier Inc. All rights reserved.
机译:将高通量数据整合到对生物网络的系统性和动态性组织的调查中一直是一项艰巨的任务。在这里,我们提出了一种简单的分层聚类算法,该算法对实现该目标大有帮助。我们的方法通过整合高通量蛋白质-蛋白质相互作用数据与所添加的亚细胞定位和表达谱数据,有效地揭示了酵母蛋白质-蛋白质相互作用网络的模块化结构,并从功能模块中区分了蛋白质复合物。此外,我们利用检测到的模块为模块中未表征的组件提供可靠的功能上下文。另一方面,各种蛋白质-蛋白质缔合信息的整合使我们的方法对于假阳性尤其是衍生蛋白质复合物的假阳性更为可靠。更重要的是,这种简单的方法可以自然地扩展到其他类型的数据融合,并为研究生物网络和其他形式的复杂网络的更全面特性提供了框架。 (c)2006 Elsevier Inc.保留所有权利。

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