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首页> 外文期刊>Journal of computational biology >Reconstruction of Biological Networks by Incorporating Prior Knowledge into Bayesian Network Models
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Reconstruction of Biological Networks by Incorporating Prior Knowledge into Bayesian Network Models

机译：通过将先验知识纳入贝叶斯网络模型来重建生物网络

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Abstract Bayesian network model is widely used for reverse engineering of biological network structures. An advantage of this model is its capability to integrate prior knowledge into the model learning process, which can lead to improving the quality of the network reconstruction outcome. Some previous works have explored this area with focus on using prior knowledge of the direct molecular links, except for a few recent ones proposing to examine the effects of molecular orderings. In this study, we propose a Bayesian network model that can integrate both direct links and orderings into the model. Random weights are assigned to these two types of prior knowledge to alleviate bias toward certain types of information. We evaluate our model performance using both synthetic data and biological data for the RAF signaling network, and illustrate the significant improvement on network structure reconstruction of the proposing models over the existing methods. We also examine the correlation between the improvem..." /> rel="meta" type="application/atom+xml" href="http://dx.doi.org/10.1089%2Fcmb.2011.0194" /> rel="meta" type="application/rdf+json" href="http://dx.doi.org/10.1089%2Fcmb.2011.0194" /> rel="meta" type="application/unixref+xml" href="http://dx.doi.org/10.1089%2Fcmb.2011.0194" /> 展开▼
机译：摘要贝叶斯网络模型被广泛用于生物网络结构的逆向工程。该模型的优势在于其将先验知识整合到模型学习过程中的能力，这可以提高网络重建结果的质量。之前的一些工作已经探索了这一领域，重点是利用对直接分子链接的先验知识，除了最近提出的一些研究分子顺序效应的研究。在这项研究中，我们提出了一种贝叶斯网络模型，该模型可以将直接链接和排序集成到模型中。随机权重分配给这两种类型的先验知识，以减轻对某些类型信息的偏见。我们使用RAF信号网络的合成数据和生物学数据评估模型性能，并说明在现有方法的基础上对提议模型的网络结构重构的重大改进。我们还研究了改进之间的相关性。“” <元名称=“ dc.Identifier” scheme =“ publisher-id” content =“ 10.1089 / cmb.2011.0194” /> <元名称=“ dc.Identifier” scheme =“ doi”内容=“ 10.1089 / cmb.2011.0194” /> <元名称=“ dc.Source” content =“ http://www.liebertpub.com/cmb” /> <元名称=“ dc。语言” content =“ zh-CN” /> <元名称=”关键字“ content =”计算分子生物学，功能基因组学“ /> rel =” meta“ type =” application / atom + xml“ href =” http://dx.doi.org /10.1089%2Fcmb.2011.0194“ /> rel =” meta“ type =” application / rdf + json“ h ref =“ http://dx.doi.org/10.1089%2Fcmb.2011.0194” /> rel =“ meta” type =“ application / unixref + xml” href =“ http://dx.doi.org/ 10.1089％2Fcmb.2011.0194“ />

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