用于个体化推断的贝叶斯协变量依赖分位数有向无环图模型
Bayesian Covariate-Dependent Quantile Directed Acyclic Graphical Models for Individualized Inference
AI总结：
提出贝叶斯协变量依赖分位数DAG模型qDAGx，利用参数扩展马蹄先验实现大规模变量下的个体化网络结构推断，克服均值回归局限，在精准医学中成功推断肺癌患者特异性的蛋白质相互作用网络。
摘要：
我们提出了一种称为“qDAGx”的方法，用于贝叶斯协变量依赖分位数有向无环图（DAGs），这些DAGs是个体化的，即它们依赖于个体特定的协变量。所提方法的个体化DAG结构可以在任意给定分位数下被唯一识别，且仅基于观测数据，无需已知拓扑排序等强假设。为了将该方法扩展至大量变量和协变量，我们为模型参数提出了一种新颖的参数扩展马蹄先验，为该方法提供了诸多理论上的和计算上的优势。通过对条件分位数进行建模，qDAGx克服了DAGs的均值回归的常见局限性，这些局限性可能对似然的选择（例如多元正态性假设）以及先验的选择敏感。我们通过广泛的数值模拟以及精准医学中的应用，即推断肺癌中患者特定的蛋白质-蛋白质相互作用网络，展示了qDAGx的性能。
英文摘要：
We propose an approach termed ``qDAGx'' for Bayesian covariate-dependent quantile directed acyclic graphs (DAGs) where these DAGs are individualized, in the sense that they depend on individual-specific covariates. The individualized DAG structure of the proposed approach can be uniquely identified at any given quantile, based on purely observational data without strong assumptions such as a known topological ordering. To scale the proposed method to a large number of variables and covariates, we propose for the model parameters a novel parameter expanded horseshoe prior that affords a number of attractive theoretical and computational benefits to our approach. By modeling the conditional quantiles, qDAGx overcomes the common limitations of mean regression for DAGs, which can be sensitive to the choice of likelihood, e.g., an assumption of multivariate normality, as well as to the choice of priors. We demonstrate the performance of qDAGx through extensive numerical simulations and via an application in precision medicine, which infers patient-specific protein--protein interaction networks in lung cancer.
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