ECHO-PPI: Trustworthy AI for Evidence-Bundled Detection of Overlapping Protein Modules in Protein-Protein Interaction Networks
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arXiv
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| Format: | Preprint |
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2026
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| author | Soltani, Sima Jalali, Mehrdad Forghani, Yahya Sheybani, Reza |
| author_facet | Soltani, Sima Jalali, Mehrdad Forghani, Yahya Sheybani, Reza |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2605_21216 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | ECHO-PPI: Trustworthy AI for Evidence-Bundled Detection of Overlapping Protein Modules in Protein-Protein Interaction Networks Soltani, Sima Jalali, Mehrdad Forghani, Yahya Sheybani, Reza Social and Information Networks 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. |
| title | ECHO-PPI: Trustworthy AI for Evidence-Bundled Detection of Overlapping Protein Modules in Protein-Protein Interaction Networks |
| topic | Social and Information Networks |
| url | https://arxiv.org/abs/2605.21216 |