ROBUST-Web

ROBUST-Web identifies disease-related modules in protein–protein interaction networks using a bias-aware Steiner tree model.

Key Features:

• Bias-Aware Steiner Tree Modeling: Incorporates bias-aware edge costs into a Steiner tree framework to correct study bias in protein–protein interaction networks and improve disease module detection.
• Integrated Functional Analysis: Performs gene set enrichment analysis, tissue expression annotation, and visualization of drug–protein and disease–gene interactions for downstream module interpretation.

Scientific Applications:

• Disease Module Identification: Enables robust discovery and functional characterization of molecular modules underlying complex diseases from protein–protein interaction data.

Methodology:

ROBUST-Web applies a Steiner tree optimization model with bias-aware edge weighting to protein–protein interaction networks, identifies connected disease modules, and integrates enrichment and annotation analyses to contextualize inferred modules.