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首页> 外文期刊>Bioinformatics >Advances to Bayesian network inference for generating causal networks from observational biological data.
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Advances to Bayesian network inference for generating causal networks from observational biological data.

机译：贝叶斯网络推论的发展，用于根据观察性生物学数据生成因果网络。

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摘要

MOTIVATION: Network inference algorithms are powerful computational tools for identifying putative causal interactions among variables from observational data. Bayesian network inference algorithms hold particular promise in that they can capture linear, non-linear, combinatorial, stochastic and other types of relationships among variables across multiple levels of biological organization. However, challenges remain when applying these algorithms to limited quantities of experimental data collected from biological systems. Here, we use a simulation approach to make advances in our dynamic Bayesian network (DBN) inference algorithm, especially in the context of limited quantities of biological data. RESULTS: We test a range of scoring metrics and search heuristics to find an effective algorithm configuration for evaluating our methodological advances. We also identify sampling intervals and levels of data discretization that allow the best recovery of the simulated networks. We develop a novel influencescore for DBNs that attempts to estimate both the sign (activation or repression) and relative magnitude of interactions among variables. When faced with limited quantities of observational data, combining our influence score with moderate data interpolation reduces a significant portion of false positive interactions in the recovered networks. Together, our advances allow DBN inference algorithms to be more effective in recovering biological networks from experimentally collected data. AVAILABILITY: Source code and simulated data are available upon request. SUPPLEMENTARY INFORMATION: http://www.jarvislab.net/Bioinformatics/BNAdvances/
机译：动机：网络推理算法是强大的计算工具，可用于从观测数据中识别变量之间的假定因果关系。贝叶斯网络推断算法具有特殊的希望，因为它们可以捕获跨多个层次的生物组织的变量之间的线性，非线性，组合，随机和其他类型的关系。但是，将这些算法应用于从生物学系统收集的有限数量的实验数据时，仍然存在挑战。在这里，我们使用一种仿真方法来改进动态贝叶斯网络（DBN）推理算法，尤其是在生物学数据数量有限的情况下。结果：我们测试了一系列的评分指标，并搜索了启发式算法，以找到一种有效的算法配置来评估我们的方法论进展。我们还将确定采样间隔和数据离散化级别，以使模拟网络得到最佳恢复。我们为DBN开发了一种新颖的Impactscore，旨在估计变量之间相互作用的符号（激活或抑制）和相对大小。当面对有限数量的观测数据时，将我们的影响力得分与适度的数据插值相结合，可以减少已恢复网络中大量误报互动。在一起，我们的进步使DBN推理算法可以更有效地从实验收集的数据中恢复生物网络。可用性：可根据要求提供源代码和模拟数据。补充信息：http://www.jarvislab.net/Bioinformatics/BNAdvances/

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