Inferring modules from human protein interactome classes.
Inferring modules from human protein interactome classes.
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DOI：
10.1186/1752-0509-4-102
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发表时间：
2010-07-23
影响因子：
--
通讯作者：
Capobianco E
中科院分区：
文献类型：
--
作者：
Marras E;Travaglione A;Chaurasia G;Futschik M;Capobianco E
蛋白质间相互作用网络的整合是系统生物学的一个重要课题。虽然蛋白质相互作用数据的整合是常规进行的，这个过程对网络分析结果的影响还没有被检查。特别是，为了优化异构交互数据集的融合，不仅要考虑它们的覆盖度和准确性，还要考虑它们的相互依赖性和其他显著特征。我们研究了这个问题的基础上检测到的模块的分析网络聚类方法应用于集成和个人（分解）的数据源，我们称之为交互类。由于类的多样性，我们处理变量的依赖关系的数据特征所产生的结构特异性和偏见，但也从可能的重叠。由于人类相互作用组的高度连接区域可能指向潜在的蛋白质复合物，我们专注于模块化的概念，并通过基于GO，MIPS和KEGG的独立验证阐明了模块提取算法的检测能力。从蛋白质与基因表达的相互作用的组合，已经提出了一个置信度评分方案，然后通过GO进行进一步的分类，在永久和瞬时模块。解聚的相互作用组被证明是信息推断模块化，从而有助于进行有效的综合分析。通过多个注释对提取的模块进行验证允许评估在蛋白质途径背景下分配给模块的置信度。值得注意的是，所提出的多层置信度方案可以用于网络校准，通过基于生物学证据实现从未加权到加权的相互作用组的过渡。
The integration of protein-protein interaction networks derived from high-throughput screening approaches and complementary sources is a key topic in systems biology. Although integration of protein interaction data is conventionally performed, the effects of this procedure on the result of network analyses has not been examined yet. In particular, in order to optimize the fusion of heterogeneous interaction datasets, it is crucial to consider not only their degree of coverage and accuracy, but also their mutual dependencies and additional salient features. We examined this issue based on the analysis of modules detected by network clustering methods applied to both integrated and individual (disaggregated) data sources, which we call interactome classes. Due to class diversity, we deal with variable dependencies of data features arising from structural specificities and biases, but also from possible overlaps. Since highly connected regions of the human interactome may point to potential protein complexes, we have focused on the concept of modularity, and elucidated the detection power of module extraction algorithms by independent validations based on GO, MIPS and KEGG. From the combination of protein interactions with gene expressions, a confidence scoring scheme has been proposed before proceeding via GO with further classification in permanent and transient modules. Disaggregated interactomes are shown to be informative for inferring modularity, thus contributing to perform an effective integrative analysis. Validation of the extracted modules by multiple annotation allows for the assessment of confidence measures assigned to the modules in a protein pathway context. Notably, the proposed multilayer confidence scheme can be used for network calibration by enabling a transition from unweighted to weighted interactomes based on biological evidence.
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影响因子：
56.9
作者：
de Lichtenberg, U;Jensen, LJ;Bork, P
通讯作者：
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影响因子：
64.8
作者：
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DOI：
10.1142/s0219720008003497
发表时间：
2008-06-01
影响因子：
1
作者：
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通讯作者：
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DOI：
10.1073/pnas.0605965104
发表时间：
2007-01-02
影响因子：
11.1
作者：
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通讯作者：
Barthelemy, Marc
影响因子：
3
作者：
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通讯作者：
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