ECHO-PPI: Trustworthy AI for Evidence-Bundled Detection of Overlapping Protein Modules in Protein-Protein Interaction Networks

TL;DR

ECHO-PPI is a framework that enhances the interpretability and confidence of overlapping protein module detection in protein-protein interaction networks by integrating multiple evidence sources.

Contribution

It introduces an evidence-bundled, interpretable approach for overlapping protein-module detection that provides confidence scores and hierarchical labels for biological insights.

Findings

ECHO-PPI maintains strong detection performance while adding interpretability.

The framework provides hierarchical confidence labels for each protein-module assignment.

Evaluation shows improved auditability and reproducibility in biological interpretation.

Abstract

Protein-protein interaction networks provide a graph-level view of cellular organization, yet their functional modules are overlapping, noisy, and difficult to interpret from cluster assignments alone. Existing community-detection methods can recover candidate protein complexes, but they rarely explain why an individual protein is assigned to a specific module or whether that assignment should be treated as core, peripheral, or uncertain. Here we introduce ECHO-PPI, an evidence-bundled framework for interpretable overlapping protein-module detection in protein-protein interaction networks. ECHO-PPI integrates weighted network topology, semantic protein profiles, and Gene Ontology evidence to identify evidence-potential nuclei, construct candidate modules, perform overlap-aware assignment, and export hierarchical confidence labels. The framework supports trustworthy computational decision support through assignment-level interpretability: each protein-module assignment is accompanied by topology, semantic, and Gene Ontology evidence scores and a hierarchical confidence label, enabling curators to inspect, rank, and triage overlapping module predictions. Evaluation on yeast protein-interaction data shows that ECHO-PPI preserves the behaviour of strong overlap-aware baselines while adding evidence-bundled auditability. Rather than claiming universal predictive superiority, ECHO-PPI addresses a complementary need: making overlapping protein-module predictions inspectable, confidence-aware, and reproducible for downstream biological interpretation.