Research graph
References from Protein-novelty, sequence-similarity, and negative-sampling controls reveal benchmark-specific discrimination in protein–protein interaction prediction. Local targets link to admitted publications; unresolved targets remain external evidence.
10.64898/2025.12.03.692055
10.64898/2025.12.03.692055 · External reference
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10.2307/2346101 · External reference
10.1093/bib/bbae076
10.1093/bib/bbae076 · External reference
10.3390/cells14181449
10.3390/cells14181449 · External reference
Topology-driven negative sampling enhances generalizability in protein–protein interaction prediction
10.1093/bioinformatics/btaf148 · 2025 · External reference
10.1093/bioinformatics/btz328
10.1093/bioinformatics/btz328 · External reference
Protein– protein interactions essentials: Key concepts to building and analyzing interactome networks
10.1371/journal.pcbi.1000807 · 2010 · External reference
10.1093/nar/gkn159
10.1093/nar/gkn159 · External reference
10.1093/bioinformatics/btu857
10.1093/bioinformatics/btu857 · External reference
Predicting protein–protein interaction with interpretable bilinear attention network
10.1016/j.cmpb.2025.108756 · 2025 · External reference
10.1093/bioinformatics/bty573
10.1093/bioinformatics/bty573 · External reference
10.1093/bioinformatics/btab737
10.1093/bioinformatics/btab737 · External reference
Protein– protein interaction prediction using bidirectional GRUs with explicit ensemble
10.1371/journal.pone.0326960 · 2025 · External reference
Pitfalls of machine learning models for protein–protein interaction networks
10.1093/bioinformatics/btae012 · 2024 · External reference
Negative sampling strategies impact the prediction of scale-free biomolecular network interactions with machine learning
10.1186/s12915-025-02231-w · 2025 · External reference
10.1186/s12859-017-1871-x
10.1186/s12859-017-1871-x · External reference
10.1126/science.ade2574
10.1126/science.ade2574 · External reference
Mapping protein–protein interactions by mass spectrometry
10.1002/mas.21887 · 2026 · External reference
Towards a more accurate and reliable evaluation of machine learning protein–protein interaction prediction model performance in the presence of unavoidable dataset biases
10.1515/jib-2024-0054 · 2025 · External reference
Dual-protein embedding-based graph model with dynamic attention for interaction prediction
10.1093/bib/bbaf517 · 2025 · External reference
Flaws in evaluation schemes for pair-input computational predictions
10.1038/nmeth.2259 · 2012 · External reference
Benchmarks in antimicrobial peptide prediction are biased due to the selection of negative data
10.1093/bib/bbac343 · 2022 · External reference
10.1038/nbt.3988
10.1038/nbt.3988 · External reference
10.1093/bib/bbae405
10.1093/bib/bbae405 · External reference
10.3390/ijms262311671
10.3390/ijms262311671 · External reference
Machine learning on protein-protein interaction prediction: models, challenges and trends
10.1093/bib/bbad076 · 2023 · External reference
10.1186/s12859-023-05594-5
10.1186/s12859-023-05594-5 · External reference
