High-dimensional interaction models are useful for studying, for example, how a large set of variables of interest, such as gene expression or other omics features, interact with a smaller set of modifying variables, such as clinical covariates. In this context, the pliable lasso has recently been proposed as an efficient method for screening large numbers of potential interaction terms under an asymmetric weak hierarchical constraint. In this work, we extend this framework by introducing PliableBVS, a Bayesian variable selection approach that preserves the hierarchical structure of the pliable lasso while inducing sparsity through spike-and-slab priors. The proposed model combines the continuous shrinkage effect of Bayesian lasso with a hierarchical spike-and-slab prior formulation that has two layers of decision variables: one governing the inclusion of main effects and another controlling the inclusion of interaction effects which is conditional on the inclusion of the corresponding main effects. This structure enables simultaneous selection of high-dimensional main and interaction effects within a coherent probabilistic framework. In simulation studies the proposed method outperforms the original pliable lasso in identifying active main and interaction effects, reducing false discoveries, and improving prediction accuracy in most scenarios. Applications with data from a labor onset study and a preeclampsia study demonstrate that PliableBVS selects biologically meaningful features and interactions.

翻译：高维交互模型在诸多研究中具有重要价值，例如探究大量感兴趣变量（如基因表达或其他组学特征）如何与少量修饰变量（如临床协变量）相互作用。在此背景下，近年来提出的pliable lasso方法能够在非对称弱层次约束下高效筛选大量潜在交互项。本文通过引入PliableBVS——一种保留pliable lasso层次结构并利用spike-and-slab先验实现稀疏化的贝叶斯变量选择方法——扩展了该框架。所提模型将贝叶斯lasso的连续收缩效应与双层决策变量的层次化spike-and-slab先验公式相结合：第一层控制主效应包含与否，第二层以对应主效应包含为条件控制交互效应包含与否。该结构能在统一的概率框架下实现高维主效应与交互效应的联合选择。模拟研究表明，在大多数场景中，所提方法在识别活跃的主效应和交互效应、减少错误发现以及提高预测精度方面均优于原始pliable lasso方法。通过一项分娩启动研究和一项子痫前期研究的数据应用，验证了PliableBVS能够筛选出具有生物学意义的特征及其交互作用。