Bayesian Hierarchical Varying-Sparsity Regression Models with Application to Cancer Proteogenomics
贝叶斯层级变稀疏回归及癌症蛋白基因组学应用
Identifying patient-specific prognostic biomarkers is of critical importance in developing personalized treatment for clinically and molecularly heterogeneous diseases such as cancer. In this article, we propose a novel regression framework, Bayesian hierarchical varying-sparsity regression (BEHAVIOR) models to select clinically relevant disease markers by integrating proteogenomic (proteomic+genomic) and clinical data. Our methods allow flexible modeling of protein–gene…