Revealing data leakage in protein interaction benchmarks

0585960 - ÚOCHB 2025 RIV AT eng C - Konferenční příspěvek (zahraniční konf.)
Bushuiev, A. - Bushuiev, Roman - Sedlář, J. - Pluskal, Tomáš - Damborský, J. - Mazurenko, S. - Šivic, J.
Revealing data leakage in protein interaction benchmarks.
GEM-2024. ICLR 2024 Workshop on Generative and Experimental Perspectives for Biomolecular Design. Vienna: ICLR, 2024.
[ICLR 2024. International Conference on Learning Representations /12./. Vienna (AT), 07.05.2024-11.05.2024]
Grant CEP: GA MŠMT(CZ) LM2023055; GA ČR(CZ) GM21-11563M
GRANT EU: European Commission(XE) 891397 - KavaTarget
Výzkumná infrastruktura: e-INFRA CZ II - 90254; RECETOX RI II - 90269
Institucionální podpora: RVO:61388963
Klíčová slova: protein-protein interactions * data leakage * benchmarks * data splits
Obor OECD: Other biological topics
https://openreview.net/forum?id=ORMXYUK5IY

In recent years, there has been remarkable progress in machine learning for protein-protein interactions. However, prior work has predominantly focused on improving learning algorithms, with less attention paid to evaluation strategies and data preparation. Here, we demonstrate that further development of machine learning methods may be hindered by the quality of existing train-test splits. Specifically, we find that commonly used splitting strategies for protein complexes, based on protein sequence or metadata similarity, introduce major data leakage. This may result in overoptimistic evaluation of generalization, as well as unfair benchmarking of the models, biased towards assessing their overfitting capacity rather than practical utility. To overcome the data leakage, we recommend constructing data splits based on 3D structural similarity of protein-protein interfaces and suggest corresponding algorithms. We believe that addressing the data leakage problem is critical for further progress in this research area.
Trvalý link: https://hdl.handle.net/11104/0353594