Quantitative assessment of gene expression network module-validation methods.
Quantitative assessment of gene expression network module-validation methods.
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DOI：
10.1038/srep15258
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发表时间：
2015-10-16
影响因子：
4.6
通讯作者：
Wang Y
中科院分区：
文献类型：
--
作者：
Li B;Zhang Y;Yu Y;Wang P;Wang Y;Wang Z;Wang Y
在不同的网络中验证多能模块在系统生物学和网络药理学方面具有巨大的潜力。一个新的挑战是如何评估从多组学网络中发现所有潜在模块的准确性，并基于超越功能富集和生物验证的创新计算方法验证其结构特征。为了展示这一领域的框架进展，我们系统地将现有的基于模块化架构的计算验证方法（CVAMA）分为基于拓扑的方法（TBA）和基于拓扑的方法（SBA）。我们比较了现有的模块验证方法的基础上，11个基因表达数据集，和部分一致的结果，在同质模型的形式得到与每一个单独的方法，而不一致的相互矛盾的结果之间发现TBA和SBA。Zsummary值的TBA具有较高的验证成功率（VSR）（51%）和较高的波动率（FR）（80.92%），而近似无偏（Au）p值的SBA具有较低的VSR（12.3%）和较低的FR（45.84%）。灰色区域模拟结果表明，两种模型的预测结果基本一致，在6个模拟水平上TBA的变异率（VR）较低（8.10%）。尽管面临着许多新的挑战和证据的限制，CVAMA可能会提供新的见解模块化网络。
Validation of pluripotent modules in diverse networks holds enormous potential for systems biology and network pharmacology. An arising challenge is how to assess the accuracy of discovering all potential modules from multi-omic networks and validating their architectural characteristics based on innovative computational methods beyond function enrichment and biological validation. To display the framework progress in this domain, we systematically divided the existing Computational Validation Approaches based on Modular Architecture (CVAMA) into topology-based approaches (TBA) and statistics-based approaches (SBA). We compared the available module validation methods based on 11 gene expression datasets, and partially consistent results in the form of homogeneous models were obtained with each individual approach, whereas discrepant contradictory results were found between TBA and SBA. The TBA of the Zsummary value had a higher Validation Success Ratio (VSR) (51%) and a higher Fluctuation Ratio (FR) (80.92%), whereas the SBA of the approximately unbiased (AU) p-value had a lower VSR (12.3%) and a lower FR (45.84%). The Gray area simulated study revealed a consistent result for these two models and indicated a lower Variation Ratio (VR) (8.10%) of TBA at 6 simulated levels. Despite facing many novel challenges and evidence limitations, CVAMA may offer novel insights into modular networks.
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影响因子：
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作者：
Marras E;Travaglione A;Chaurasia G;Futschik M;Capobianco E
通讯作者：
Capobianco E
影响因子：
--
作者：
Sridharan GV;Yi M;Hassoun S;Lee K
通讯作者：
Lee K
影响因子：
3
作者：
Zhang M;Lu LJ
通讯作者：
Lu LJ
影响因子：
--
作者：
Qi J;Michoel T;Butler G
通讯作者：
Butler G