PliableBVS: A flexible Bayesian variable selection method for modeling interactions with mandatory modifying variables
PliableBVS:为含强制修饰变量的交互建模设计的灵活贝叶斯变量选择方法
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…