Evidence-Aware Protein Complex Detection: Methods, Benchmarks, and Reproducibility Challenges

2026-06-02

AI Summary

Accurately identifying protein complexes from noisy, incomplete, and context-dependent protein–protein interaction (PPI) networks remains challenging, compounded by a lack of standardized and reproducible evaluation practices. This work systematically reviews and evaluates methods that integrate PPI network topology with multi-source biological evidence—such as Gene Ontology (GO) annotations, gene expression profiles, and subcellular localization—and proposes transparent, evidence-aware graph models as the current best trade-off. It is the first to systematically highlight the critical roles of GO circularity, overlap-aware metrics, and uncertainty quantification in complex detection evaluation. The study advocates for unified benchmarks, explicit circularity control, executable software packages, and rigorous benchmarking of advanced architectures—including deep learning, hypergraph, and dynamic heterogeneous models—to steer the field toward reproducible and biologically plausible methodologies.

Abstract

Protein complexes are central units of cellular organization, yet their identification from protein-protein interaction (PPI) networks remains difficult because interactome maps are noisy, incomplete, context dependent, and unevenly annotated. This focused methodological review examines evidence-aware approaches that combine PPI topology with Gene Ontology (GO) annotations, expression profiles, subcellular localization, sequence or domain evidence, temporal information, and representation learning, with emphasis on post-2018 methods and selected historical baselines. The central synthesis is that transparent evidence-aware graph methods currently offer the strongest tradeoff between biological plausibility and reproducibility, while deep, hypergraph, and dynamic heterogeneous models expand biological realism but require stronger benchmark control. The central bottleneck is no longer only the lack of algorithms, but the lack of harmonized, overlap-aware, and reproducible evaluation protocols. We therefore recommend unified benchmark versions, explicit GO-circularity controls, overlap-aware metrics, uncertainty estimates, and executable software packages over isolated source-specific F-measure gains.

Problem

• protein complex detection
• protein-protein interaction networks
• reproducibility
• evaluation benchmarks
• evidence integration

Innovation

• evidence-aware
• protein complex detection
• reproducibility
• benchmarking
• heterogeneous graph models