Evidence-Aware Protein Complex Detection: Methods, Benchmarks, and Reproducibility Challenges
For researchers in computational biology, this paper highlights the need for standardized benchmarks and reproducibility in protein complex detection, but its contribution is primarily a synthesis and critique rather than a novel method or result.
This review examines evidence-aware protein complex detection methods, finding that transparent graph methods offer the best tradeoff between biological plausibility and reproducibility, while the main bottleneck is the lack of harmonized, reproducible evaluation protocols rather than algorithmic innovation.
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.