ECHO-PPI: Evidence-Bundled Overlapping Protein Module Detection with Hierarchical Assignment Confidence for Network Biology

Abstract

Identifying protein modules in protein-protein interaction (PPI) networks is central to understanding cellular organisation, yet many community-detection methods treat module membership as a binary output with limited assignment-level justification. Proteins that participate in multiple complexes as shared subunits, peripheral interactors, or context-dependent bridges can be overlooked when networks are forced into hard partitions, and even overlapping methods rarely provide traceable evidence for individual protein-module assignments. We present ECHO-PPI, a framework for overlapping protein-module detection that combines competitive module discovery with structured assignment-level interpretation. For every protein-module assignment, ECHO-PPI exports an evidence bundle combining weighted topology, semantic functional similarity, Gene Ontology support, provenance fields, and hierarchical confidence labels: Core, Inner, Outer, and Uncertain. This makes each assignment inspectable and reproducible rather than an opaque membership claim. We benchmark ECHO-PPI on two yeast PPI resources, the Gavin socioaffinity network and the Krogan 2006 dataset, against MCL, MCL+overlap, ClusterONE, and SLPA. ECHO-PPI achieves predictive parity with overlap-aware baselines while being the only evaluated method to provide complete required-field evidence bundles. Core assignments show the strongest gold-standard support and consistent multi-channel evidence across both datasets. By separating predictive clustering from evidence-bundled interpretation, ECHO-PPI provides computational biologists with a path from cluster lists to defensible, reproducible protein-module hypotheses suitable for curator-facing network-biology workflows.