ArticleBioinformatics (Oxford, England)2011
Revisiting the negative example sampling problem for predicting protein-protein interactions.
Article in Bioinformatics (Oxford, England), 2011. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 29 papers.
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29 citing papers in PubMed.
- 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
- Interpretable deep learning framework for mapping E3-substrate binding interfaces.Nature communications · 2026Article
- DensePPI-2: a bio-inspired update for sequence-based PPI prediction leveraging mutation rates.Briefings in bioinformatics · 2025Article
- Negative sampling strategies impact the prediction of scale-free biomolecular network interactions with machine learning.BMC biology · 2025Article
- Pitfalls of machine learning models for protein-protein interaction networks.Bioinformatics (Oxford, England) · 2024Article
- Assessment of community efforts to advance network-based prediction of protein-protein interactions.Nature communications · 2023Article
- Protein-protein interaction and non-interaction predictions using gene sequence natural vector.Communications biology · 2022Article
- Systematic auditing is essential to debiasing machine learning in biology.Communications biology · 2021Article
- AnOxPePred: using deep learning for the prediction of antioxidative properties of peptides.Scientific reports · 2020Article
- Improved cytokine-receptor interaction prediction by exploiting the negative sample space.BMC bioinformatics · 2020Article
- Machine learning-based chemical binding similarity using evolutionary relationships of target genes.Nucleic acids research · 2019Article
- Neglog: Homology-Based Negative Data Sampling Method for Genome-Scale Reconstruction of Human Protein-Protein Interaction Networks.International journal of molecular sciences · 2019Article
- Recent applications of deep learning and machine intelligence on in silico drug discovery: methods, tools and databases.Briefings in bioinformatics · 2019Review
- Combining learning and constraints for genome-wide protein annotation.BMC bioinformatics · 2019Article
- Pioneering topological methods for network-based drug-target prediction by exploiting a brain-network self-organization theory.Briefings in bioinformatics · 2018Article
- IDPpi: Protein-Protein Interaction Analyses of Human Intrinsically Disordered Proteins.Scientific reports · 2018Article
- Sequence-based prediction of protein protein interaction using a deep-learning algorithm.BMC bioinformatics · 2017Article
- Non-interacting proteins may resemble interacting proteins: prevalence and implications.Scientific reports · 2017Article
- Evolution of protein-protein interaction networks in yeast.PloS one · 2017Article
- A novel one-class SVM based negative data sampling method for reconstructing proteome-wide HTLV-human protein interaction networks.Scientific reports · 2015Article
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2 authors.
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Abstract
motivationA number of computational methods have been proposed that predict protein-protein interactions (PPIs) based on protein sequence features. Since the number of potential non-interacting protein pairs (negative PPIs) is very high both in absolute terms and in comparison to that of interacting protein pairs (positive PPIs), computational prediction methods rely upon subsets of negative PPIs for training and validation. Hence, the need arises for subset sampling for negative PPIs.
resultsWe clarify that there are two fundamentally different types of subset sampling for negative PPIs. One is subset sampling for cross-validated testing, where one desires unbiased subsets so that predictive performance estimated with them can be safely assumed to generalize to the population level. The other is subset sampling for training, where one desires the subsets that best train predictive algorithms, even if these subsets are biased. We show that confusion between these two fundamentally different types of subset sampling led one study recently published in Bioinformatics to the erroneous conclusion that predictive algorithms based on protein sequence features are hardly better than random in predicting PPIs. Rather, both protein sequence features and the 'hubbiness' of interacting proteins contribute to effective prediction of PPIs. We provide guidance for appropriate use of random versus balanced sampling. AVAILABILITY: The datasets used for this study are available at http://www.marcottelab.org/PPINegativeDataSampling. CONTACT: yungki@mail.utexas.edu; marcotte@icmb.utexas.edu. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
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