Module networks revisited: computational assessment and prioritization of model predictions
Module networks revisited: computational assessment and prioritization of model predictions
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DOI：
10.1093/bioinformatics/btn658
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发表时间：
2009-02-15
期刊：
影响因子：
5.8
通讯作者：
Michoel, Tom
中科院分区：
文献类型：
--
作者：
Joshi, Anagha;De Smet, Riet;Michoel, Tom
动机：计算生物学中高维推理和预测问题的解决方案几乎总是数学理论和实际约束（例如有限的计算资源）之间的折衷。随着时间的推移，计算能力的增加，但完善的推理方法往往保持锁定在其最初的次优solution.Results：我们重新审视西格尔等人的方法。推断调控模块和他们的条件特异性调节基因表达数据。与他们直接基于优化的解决方案相比，我们使用从可能的统计模型集合中提取的更具代表性的质心解决方案来解释数据。集成方法自动选择最具信息量的基因的子集，并为它们构建定量上更好的模型。在大多数模型中聚集在一起的基因产生功能上更连贯的模块。调节器一致地分配到一个模块更经常地由文献支持，但一个单一的模型总是包含许多调节器分配不支持的合奏。可靠地检测特定条件或组合调节是特别困难的，在一个单一的最佳，但可以实现使用合奏平均。
Motivation: The solution of high-dimensional inference and prediction problems in computational biology is almost always a compromise between mathematical theory and practical constraints, such as limited computational resources. As time progresses, computational power increases but well-established inference methods often remain locked in their initial suboptimal solution.Results: We revisit the approach of Segal et al. to infer regulatory modules and their condition-specific regulators from gene expression data. In contrast to their direct optimization-based solution, we use a more representative centroid-like solution extracted from an ensemble of possible statistical models to explain the data. The ensemble method automatically selects a subset of most informative genes and builds a quantitatively better model for them. Genes which cluster together in the majority of models produce functionally more coherent modules. Regulators which are consistently assigned to a module are more often supported by literature, but a single model always contains many regulator assignments not supported by the ensemble. Reliably detecting condition-specific or combinatorial regulation is particularly hard in a single optimum but can be achieved using ensemble averaging.