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

2026-05-20

AI Summary

This study addresses the limited interpretability of existing methods for detecting functional modules in protein–protein interaction (PPI) networks, which often fail to explain why a protein is assigned to a module or to distinguish core, peripheral, and ambiguous members. To overcome these limitations, the authors propose ECHO-PPI, a novel framework that introduces an evidence-bundling mechanism and hierarchical confidence labels. By integrating weighted network topology, semantic protein profiles, and Gene Ontology evidence, ECHO-PPI enables interpretable and auditable detection of overlapping modules. The method maintains competitive detection performance while substantially enhancing the reliability and explainability of predictions, thereby facilitating downstream biological validation and manual prioritization.

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.

Problem

• overlapping protein modules
• protein-protein interaction networks
• interpretable AI
• module assignment confidence
• evidence-based interpretation

Innovation

• interpretable AI
• overlapping community detection
• evidence-bundled framework
• protein-protein interaction networks
• confidence-aware assignment