ArticleBMC bioinformatics2006
Choosing negative examples for the prediction of protein-protein interactions.
Article in BMC bioinformatics, 2006. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 100 papers.
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Who cites it
100 citing papers in PubMed.
- Protein language models are accidental taxonomists.BMC bioinformatics · 2026Article
- Interpretable deep learning framework for mapping E3-substrate binding interfaces.Nature communications · 2026Article
- Predicting Protein-Protein Interactions by Convolutional Neural Network Model.Biotech (Basel (Switzerland)) · 2026Article
- Sequence-Based Protein-Protein Interaction Prediction and Its Applications in Drug Discovery.Cells · 2025Review
- Enhancing cross-domain protein and peptide interaction with retrained deep learning models.Briefings in bioinformatics · 2025Article
- Evaluating sequence and structural similarity metrics for predicting shared paralog functions.NAR genomics and bioinformatics · 2025Article
- Topology-driven negative sampling enhances generalizability in protein-protein interaction prediction.Bioinformatics (Oxford, England) · 2025Article
- Article
- Prediction of virus-host associations using protein language models and multiple instance learning.PLoS computational biology · 2024Article
- A predictive approach for host-pathogen interactions using deep learning and protein sequences.Virusdisease · 2024Article
- Guiding questions to avoid data leakage in biological machine learning applications.Nature methods · 2024Review
- Pitfalls of machine learning models for protein-protein interaction networks.Bioinformatics (Oxford, England) · 2024Article
- Cracking the black box of deep sequence-based protein-protein interaction prediction.Briefings in bioinformatics · 2024Article
- Speos: an ensemble graph representation learning framework to predict core gene candidates for complex diseases.Nature communications · 2023Article
- Review
- Prediction of viral-host interactions of COVID-19 by computational methods.Chemometrics and intelligent laboratory systems : an international journal sponsored by the Chemometrics Society · 2022Article
- Protein-protein interaction and non-interaction predictions using gene sequence natural vector.Communications biology · 2022Article
- Efficient link prediction in the protein-protein interaction network using topological information in a generative adversarial network machine learning model.BMC bioinformatics · 2022Article
- Deciphering the Interactions of SARS-CoV-2 Proteins with Human Ion Channels Using Machine-Learning-Based Methods.Pathogens (Basel, Switzerland) · 2022Article
- A Web Server for GPCR-GPCR Interaction Pair Prediction.Frontiers in endocrinology · 2022Article
40 more citing papers are in PubMed but not listed here.
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Authors and funding
2 authors.
Funding
Abstract
The protein-protein interaction networks of even well-studied model organisms are sketchy at best, highlighting the continued need for computational methods to help direct experimentalists in the search for novel interactions. This need has prompted the development of a number of methods for predicting protein-protein interactions based on various sources of data and methodologies. The common method for choosing negative examples for training a predictor of protein-protein interactions is based on annotations of cellular localization, and the observation that pairs of proteins that have different localization patterns are unlikely to interact. While this method leads to high quality sets of non-interacting proteins, we find that this choice can lead to biased estimates of prediction accuracy, because the constraints placed on the distribution of the negative examples makes the task easier. The effects of this bias are demonstrated in the context of both sequence-based and non-sequence based features used for predicting protein-protein interactions.
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