ArticleBriefings in bioinformatics2024
Cracking the black box of deep sequence-based protein-protein interaction prediction.
Article in Briefings in bioinformatics, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 55 papers.
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Who cites it
55 citing papers in PubMed.
- ESpma: A method for assessing biological/non-biological interfaces using point-clouds-based structural features and protein language models.Protein science : a publication of the Protein Society · 2026Article
- Benchmarking the impact of data leakage on the performance of knowledge graph embedding models for biomedical link prediction.Bioinformatics (Oxford, England) · 2026Article
- Reliable evaluation and learning in multi-input biological association prediction.Briefings in bioinformatics · 2026Article
- Protein language models are accidental taxonomists.BMC bioinformatics · 2026Article
- Improving protein and protein interactions using pseudo-dimers derived from monomeric proteins.Nature communications · 2026Article
- ProteomeLM: A proteome-scale language model enables accurate and rapid prediction of protein-protein interactions and gene essentiality across taxa.Proceedings of the National Academy of Sciences of the United States of America · 2026Article
- Graph and Hypergraph Theories Applied to Dynamic Protein-Protein Interaction Network Analysis, and Deep-Learning Frameworks for Protein Complex Network Prediction.International journal of molecular sciences · 2026Review
- The role of protein embeddings for protein-protein interaction prediction with graph neural networks.Briefings in bioinformatics · 2026Article
- Rapid Proteome-Wide Discovery of Protein-Protein Interactions With ppIRIS.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026Article
- Generalizable deep-learning-based mRNA-protein interaction prediction strongly depends on protein diversity.Journal of cheminformatics · 2026Article
- Enhancing mutation impact prediction in protein-protein interactions through interpretable graph-based multi-level feature interactions.Bioinformatics (Oxford, England) · 2026Article
- DSS-PPI: a self-supervised graph learning framework for protein-protein interaction prediction via multimodal sequence semantics.BMC genomics · 2026Article
- A paired sequence language model for protein-protein interaction modeling.Nature communications · 2026Article
- Addendum: Data splitting against information leakage with DataSAIL.Nature communications · 2026Article
- Protein Language Models: Applications and Perspectives.Journal of proteome research · 2026Review
- Learning the language of protein-protein interactions.Nature communications · 2026Article
- Scalable embedding fusion with protein language models: insights from benchmarking text-integrated representations.Briefings in bioinformatics · 2026Article
- Revisiting co-expression-based automated function prediction in yeast with neural networks and updated Gene Ontology annotations.PloS one · 2026Article
- AI-Driven De Novo Binder Design: From Structure Prediction to Closed-Loop Optimization.Computational and structural biotechnology journal · 2026Review
- Beyond Random Splits: Assessing the Generalization of Graph and Vector Models for WT-Structure-Only Drug Resistance Prediction under Protein-Disjoint Evaluation.Computational and structural biotechnology journal · 2026Article
Corrections and comments
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Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Identifying protein-protein interactions (PPIs) is crucial for deciphering biological pathways. Numerous prediction methods have been developed as cheap alternatives to biological experiments, reporting surprisingly high accuracy estimates. We systematically investigated how much reproducible deep learning models depend on data leakage, sequence similarities and node degree information, and compared them with basic machine learning models. We found that overlaps between training and test sets resulting from random splitting lead to strongly overestimated performances. In this setting, models learn solely from sequence similarities and node degrees. When data leakage is avoided by minimizing sequence similarities between training and test set, performances become random. Moreover, baseline models directly leveraging sequence similarity and network topology show good performances at a fraction of the computational cost. Thus, we advocate that any improvements should be reported relative to baseline methods in the future. Our findings suggest that predicting PPIs remains an unsolved task for proteins showing little sequence similarity to previously studied proteins, highlighting that further experimental research into the 'dark' protein interactome and better computational methods are needed.
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