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首页> 外文期刊>Journal of Bioinformatics and Computational Biology >INFERENCE OF GENETIC NETWORKS USING LPMs: ASSESSMENT OF CONFIDENCE VALUES OF REGULATIONS
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INFERENCE OF GENETIC NETWORKS USING LPMs: ASSESSMENT OF CONFIDENCE VALUES OF REGULATIONS

机译：使用LPM的遗传网络推论：规章制度的置信度评估

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摘要

When we apply inference methods based on a set of differential equations into actual genetic network inference problems, we often end up with a large number of false-positive regulations. However, as we must check the inferred regulations through biochemical experiments, fewer false-positive regulations are preferable. In order to reduce the number of regulations checked, this study proposes a new method that assigns confidence values to all of the regulations contained in the target network. For this purpose, we combine a residual bootstrap method with the existing method, i.e. the inference method using linear programming machines (LPMs). Through numerical experiments on an artificial genetic network inference problem, we confirmed that most of the regulations with high confidence values are actually present in the target networks. We then used the proposed method to analyze the bacterial SOS DNA repair system, and succeeded in assigning reasonable confidence values to its regulations. Althoughthis study combined the bootstrap method with the inference method using the LPMs, the proposed bootstrap approach could be combined with any method that has an ability to infer a genetic network from time-series of gene expression levels.
机译：当我们将基于一组微分方程的推理方法应用于实际的遗传网络推理问题时，我们常常会遇到大量错误肯定的规定。但是，由于我们必须通过生化实验检查推断的法规，因此假阳性法规越少越可取。为了减少检查的法规数量，本研究提出了一种新方法，该方法为目标网络中包含的所有法规分配置信度值。为此，我们将残差自举方法与现有方法（即使用线性编程机（LPM）的推理方法）相结合。通过对人工遗传网络推断问题的数值实验，我们确认目标网络中实际上存在大多数具有高置信度值的规则。然后，我们使用所提出的方法来分析细菌SOS DNA修复系统，并成功地为其法规分配了合理的置信度值。尽管本研究将引导程序方法与使用LPM的推理方法相结合，但建议的引导程序方法可以与能够从基因表达水平的时间序列推断出遗传网络的任何方法相结合。

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