基因调控网络（GRN）是推断调节生物过程的分子之间复杂相互作用的有效工具，因此可以深入了解生物系统的驱动因素。推断共表达网络是 GRN 推断的关键要素，因为表达模式之间的相关性可能表明基因受到共同因素的共同调节。然而，估计共表达网络的方法通常会得出代表群体平均调节特性的聚合网络，因此无法完全捕获群体异质性。 BONOBO（通过同化组学数据获得的贝叶斯优化网络）是一种可扩展的贝叶斯模型，用于导出个体样本特异性共表达矩阵，该矩阵可识别个体之间分子相互作用的变化。对于每个样本，BONOBO 假设对数变换的中心基因表达呈高斯分布，并假设由数据中所有其他样本构建的样本特异性共表达矩阵呈共轭先验分布。将样本特异性基因共表达与先验分布相结合，BONOBO 产生样本特异性共表达矩阵后验分布的封闭式解，从而允许分析大型数据集。我们证明了 BONOBO 在多种情况下的实用性，包括分析酵母转录因子敲除研究中的基因调控、人类乳腺癌亚型中 miRNA-mRNA 相互作用的预后意义，以及人类甲状腺组织内基因调控的性别差异。我们发现 BONOBO 优于其他用于样本特异性共表达网络推理的方法，并提供了对生物过程驱动因素的个体差异的洞察。由冷泉港实验室出版社出版。

Gene regulatory networks (GRNs) are effective tools for inferring complex interactions between molecules that regulate biological processes and hence can provide insights into drivers of biological systems. Inferring coexpression networks is a critical element of GRN inference, as the correlation between expression patterns may indicate that genes are coregulated by common factors. However, methods that estimate coexpression networks generally derive an aggregate network representing the mean regulatory properties of the population and so fail to fully capture population heterogeneity. BONOBO (Bayesian Optimized Networks Obtained By assimilating Omics data) is a scalable Bayesian model for deriving individual sample-specific coexpression matrices that recognizes variations in molecular interactions across individuals. For each sample, BONOBO assumes a Gaussian distribution on the log-transformed centered gene expression and a conjugate prior distribution on the sample-specific coexpression matrix constructed from all other samples in the data. Combining the sample-specific gene coexpression with the prior distribution, BONOBO yields a closed-form solution for the posterior distribution of the sample-specific coexpression matrices, thus allowing the analysis of large datasets. We demonstrate BONOBO's utility in several contexts, including analyzing gene regulation in yeast transcription factor knockout studies, the prognostic significance of miRNA-mRNA interaction in human breast cancer subtypes, and sex differences in gene regulation within human thyroid tissue. We find that BONOBO outperforms other methods that have been used for sample-specific coexpression network inference and provides insight into individual differences in the drivers of biological processes.Published by Cold Spring Harbor Laboratory Press.