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• Profile graphical models are a novel class of graphical models that represent how an external factor influences the dependence structure of a multivariate set of variables within a single graph.method 95%
• Profile graphical models generalize both multiple graphs and chain graphs, including them as special cases.technical 90%
• The Bayesian approach for Gaussian undirected profile graphical models (BPUGM) based on continuous spike-and-slab priors learns shared sparsity structures across different levels of a risk factor.method 85%
• BPUGM infers more parsimonious networks and captures greater patient heterogeneity than the competing method GemBag in the analysis of acute myeloid leukemia protein data.result 80%
• BPUGM substantially outperforms GemBag in edge detection and model selection stability, particularly in small-sample groups.result 85%
• Profile undirected graphical models are independence-compatible with two-block LWF chain graph models.technical 90%
• A fast EM algorithm is designed for efficient inference in profile graphical models.method 90%
• The induced multiple-graph representation from competing methods like GemBag is harder to read and misses profile-specific independencies compared to the single-graph representation of profile graphical models.limitation 75%

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