ArticleMolecules (Basel, Switzerland)2021
Benchmark Evaluation of Protein-Protein Interaction Prediction Algorithms.
Article in Molecules (Basel, Switzerland), 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 26 papers.
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Who cites it
26 citing papers in PubMed.
- Generalizable deep-learning-based mRNA-protein interaction prediction strongly depends on protein diversity.Journal of cheminformatics · 2026Article
- A graph clustering algorithm with hypergraph learning and a core-attachment strategy for protein complex identification.Frontiers in genetics · 2026Article
- Sequence-Based Protein-Protein Interaction Prediction and Its Applications in Drug Discovery.Cells · 2025Review
- The coming era of proteomics-driven precision medicine.National science review · 2025Review
- ESM2_AMP: an interpretable framework for protein-protein interactions prediction and biological mechanism discovery.Briefings in bioinformatics · 2025Article
- Unravelling the human taste receptor interactome: machine learning and molecular modelling insights into protein-protein interactions.NPJ science of food · 2025Article
- Topology-driven negative sampling enhances generalizability in protein-protein interaction prediction.Bioinformatics (Oxford, England) · 2025Article
- Predictomes, a classifier-curated database of AlphaFold-modeled protein-protein interactions.Molecular cell · 2025Article
- Structural insights into Beclin 1 interactions with it's regulators for autophagy modulation.Computational and structural biotechnology journal · 2025Review
- SpatialPPIv2: Enhancing protein-protein interaction prediction through graph neural networks with protein language models.Computational and structural biotechnology journal · 2025Article
- SpatialPPI: Three-dimensional space protein-protein interaction prediction with AlphaFold Multimer.Computational and structural biotechnology journal · 2024Article
- Heterogeneous network approaches to protein pathway prediction.Computational and structural biotechnology journal · 2024Review
- Guiding questions to avoid data leakage in biological machine learning applications.Nature methods · 2024Review
- INTREPPPID-an orthologue-informed quintuplet network for cross-species prediction of protein-protein interaction.Briefings in bioinformatics · 2024Article
- A Computational Pipeline for Accurate Prioritization of Protein-Protein Binding Candidates in High-Throughput Protein Libraries.Angewandte Chemie (International ed. in English) · 2024Article
- Predictomes: A classifier-curated database of AlphaFold-modeled protein-protein interactions.bioRxiv : the preprint server for biology · 2024Article
- CombFold: predicting structures of large protein assemblies using a combinatorial assembly algorithm and AlphaFold2.Nature methods · 2024Article
- Pitfalls of machine learning models for protein-protein interaction networks.Bioinformatics (Oxford, England) · 2024Article
- Sifting Through the Noise: A Computational Pipeline for Accurate Prioritization of Protein-Protein Binding Candidates in High-Throughput Protein Libraries.bioRxiv : the preprint server for biology · 2024Article
- ABAG-docking benchmark: a non-redundant structure benchmark dataset for antibody-antigen computational docking.Briefings in bioinformatics · 2024Article
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2 authors.
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Abstract
Protein-protein interactions (PPIs) perform various functions and regulate processes throughout cells. Knowledge of the full network of PPIs is vital to biomedical research, but most of the PPIs are still unknown. As it is infeasible to discover all of them experimentally due to technical and resource limitations, computational prediction of PPIs is essential and accurately assessing the performance of algorithms is required before further application or translation. However, many published methods compose their evaluation datasets incorrectly, using a higher proportion of positive class data than occuring naturally, leading to exaggerated performance. We re-implemented various published algorithms and evaluated them on datasets with realistic data compositions and found that their performance is overstated in original publications; with several methods outperformed by our control models built on 'illogical' and random number features. We conclude that these methods are influenced by an over-characterization of some proteins in the literature and due to scale-free nature of PPI network and that they fail when tested on all possible protein pairs. Additionally, we found that sequence-only-based algorithms performed worse than those that employ functional and expression features. We present a benchmark evaluation of many published algorithms for PPI prediction. The source code of our implementations and the benchmark datasets created here are made available in open source.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.