Prior-informed conditional Gaussian graphical models: an application to protein interaction network reconstruction

2026-06-30

AI Summary

Existing methods for modeling protein–protein interaction networks often neglect prior biological knowledge and assume a static network structure across individuals, thereby failing to capture covariate-driven, personalized interaction patterns. This work proposes a conditional Gaussian graphical model that, for the first time, integrates database-derived priors and covariate-dependent relationships within a unified framework. By employing structured weighted L1 regularization, the method simultaneously incorporates population-level priors while preserving context-specific perturbations. It effectively distinguishes between universal interactions and disease-specific alterations. Applied to proteomic data from the UK Biobank (n = 49,129), the approach identified 34 network centrality–based biomarkers and six functionally coherent protein modules, with several biomarkers detectable only through connectivity changes rather than differential expression.

Abstract

Protein-protein interaction (PPI) networks, estimated from high-throughput omics data, foster biomarker discovery and precision medicine. Gaussian graphical models (GGMs) offer a principled reconstruction framework. Yet, existing applications face two limitations: they overlook the rich existing knowledge encoded in curated biological databases, and they assume a homogeneous network structure across all individuals, neglecting the influence of covariates or confounding factors on these interactions and preventing personalised representations. Even though these limitations have been addressed separately in previous work, no current approach resolves them simultaneously. We introduce a prior-informed conditional Gaussian graphical model that integrates database-derived interaction priors with covariate-dependent network modeling in a unified, scalable framework. The key methodological innovation is a structured, weighted penalty that selectively incorporates priors into population-level network estimation, while leaving context-specific perturbations entirely data-driven, as curated databases capture canonical interactions rather than disease-specific signals. Simulation studies demonstrate consistent and robust improvements in population-level network reconstruction across diverse settings, even when prior knowledge is imperfect. Applied to UK Biobank cardiometabolic proteomics (n = 49,129, p = 366 proteins), the method recovers T2D-associated network perturbations, identifying 34 network-central candidate biomarkers, several detectable only through their connectivity, not differential expression, and revealing six biologically coherent protein communities with distinct pathway enrichments spanning metabolic, cardiovascular, and cancer-related processes. Code is available at https://github.com/AlessiaMapelli/Prior-informed-conditional-GGMs.

Problem

• Protein-protein interaction networks
• Gaussian graphical models
• Prior knowledge integration
• Covariate-dependent networks
• Personalized network reconstruction

Innovation

• prior-informed GGM
• conditional Gaussian graphical model
• protein-protein interaction network
• covariate-dependent network
• structured penalty