Advances to Bayesian network inference for generating causal networks from observational biological data
Advances to Bayesian network inference for generating causal networks from observational biological data
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DOI：
10.1093/bioinformatics/bth448
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发表时间：
2004-12-12
期刊：
影响因子：
5.8
通讯作者：
Jarvis, ED
中科院分区：
文献类型：
--
作者：
Yu, J;Smith, VA;Jarvis, ED
动机：网络推理算法是强大的计算工具，用于从观测数据中识别变量之间假定的因果交互作用。贝叶斯网络推理算法具有特别的前景，因为它们可以捕捉生物组织的多个层次上的变量之间的线性、非线性、组合、随机和其他类型的关系。然而，将这些算法应用于从生物系统收集的有限数量的实验数据时，仍然存在挑战。在这里，我们使用模拟的方法来改进我们的动态贝叶斯网络(DBN)推理算法，特别是在生物数据量有限的情况下。结果：我们测试了一系列评分度量和搜索启发式算法，为评估我们的方法进步找到了一个有效的算法配置。我们还确定了采样间隔和数据离散化级别，以实现模拟网络的最佳恢复。我们为DBN开发了一种新的影响分数，它试图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，将我们的影响评分与适度的数据内插相结合，可以减少恢复网络中很大一部分误报交互。总之，我们的进步使DBN推理算法能够更有效地从实验收集的数据中恢复生物网络。
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 influence score 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.