A statistical physics approach to integrating multi-omics data for disease-module detection.

ABSTRACT: Genes associated with the same disease frequently engage in mutual biological interactions, e.g., perturbation within a specific neighborhood in the molecular interactome, often referred to as the disease module. This has propelled the advancement of network-based approaches toward elucidating the molecular bases of human diseases. Although many computational methods have been developed to integrate the molecular interactome and omics profiles to extract such context-dependent disease modules, approaches that leverage multi-omics for disease-module detection are still lacking. Here, we developed a statistical physics approach based on the random-field O(n) model (RFOnM) to fill this gap. We applied the RFOnM approach to integrate gene-expression data and genome-wide association studies or

SUBMITTER: Wang XW 

PROVIDER: S-EPMC12570349 | biostudies-literature | 2025 Oct

REPOSITORIES: biostudies-literature