ArticleBMC bioinformatics2006
Quantitative prediction of mouse class I MHC peptide binding affinity using support vector machine regression (SVR) models.
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 43 papers.
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Who cites it
43 citing papers in PubMed.
- Autonomic Nervous System Dysfunctions as a Basis for a Predictive Model of Risk of Neurological Disorders in Subjects with Prior History of Traumatic Brain Injury: Implications in Alzheimer's Disease.Journal of Alzheimer's disease : JAD · 2017Trial
- Predicting Antidiabetic Peptide Activity: A Machine Learning Perspective on Type 1 and Type 2 Diabetes.International journal of molecular sciences · 2024Article
- Multi-trait modeling and machine learning discover new markers associated with stem traits in alfalfa.Frontiers in plant science · 2024Article
- Multi-epitope vaccines: a promising strategy against viral diseases in swine.Frontiers in cellular and infection microbiology · 2024Review
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- Machine Learning Techniques for the Prediction of B-Cell and T-Cell Epitopes as Potential Vaccine Targets with a Specific Focus on SARS-CoV-2 Pathogen: A Review.Pathogens (Basel, Switzerland) · 2022Review
- Physicochemical Heuristics for Identifying High Fidelity, Near-Native Structural Models of Peptide/MHC Complexes.Frontiers in immunology · 2022Article
- PredMHC: An Effective Predictor of Major Histocompatibility Complex Using Mixed Features.Frontiers in genetics · 2022Article
- Strategies to Increase Prediction Accuracy in Genomic Selection of Complex Traits in Alfalfa (Cells · 2021Review
- Predicting MHC class I binder: existing approaches and a novel recurrent neural network solution.Briefings in bioinformatics · 2021Article
- Predicting MHC I restricted T cell epitopes in mice with NAP-CNB, a novel online tool.Scientific reports · 2021Article
- Deimmunization of protein therapeutics - Recent advances in experimental and computational epitope prediction and deletion.Computational and structural biotechnology journal · 2021Review
- Immunoinformatics: Predicting Peptide-MHC Binding.Annual review of biomedical data science · 2020Article
- Computational Modeling and Analysis to Predict Intracellular Parasite Epitope Characteristics Using Random Forest Technique.Iranian journal of public health · 2020Article
- Prediction of Anticancer Peptides Using a Low-Dimensional Feature Model.Frontiers in bioengineering and biotechnology · 2020Article
- Novel Strategies for Malaria Vaccine Design.Frontiers in immunology · 2018Review
- Novel classification for global gene signature model for predicting severity of systemic sclerosis.PloS one · 2018Article
- Deep convolutional neural networks for pan-specific peptide-MHC class I binding prediction.BMC bioinformatics · 2017Article
- Fundamentals and Methods for T- and B-Cell Epitope Prediction.Journal of immunology research · 2017Review
- Accurate prediction of immunogenic T-cell epitopes from epitope sequences using the genetic algorithm-based ensemble learning.PloS one · 2015Article
Corrections and comments
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundThe binding between peptide epitopes and major histocompatibility complex proteins (MHCs) is an important event in the cellular immune response. Accurate prediction of the binding between short peptides and the MHC molecules has long been a principal challenge for immunoinformatics. Recently, the modeling of MHC-peptide binding has come to emphasize quantitative predictions: instead of categorizing peptides as "binders" or "non-binders" or as "strong binders" and "weak binders", recent methods seek to make predictions about precise binding affinities.
resultsWe developed a quantitative support vector machine regression (SVR) approach, called SVRMHC, to model peptide-MHC binding affinities. As a non-linear method, SVRMHC was able to generate models that out-performed existing linear models, such as the "additive method". By adopting a new "11-factor encoding" scheme, SVRMHC takes into account similarities in the physicochemical properties of the amino acids constituting the input peptides. When applied to MHC-peptide binding data for three mouse class I MHC alleles, the SVRMHC models produced more accurate predictions than those produced previously. Furthermore, comparisons based on Receiver Operating Characteristic (ROC) analysis indicated that SVRMHC was able to out-perform several prominent methods in identifying strongly binding peptides.
conclusionAs a method with demonstrated performance in the quantitative modeling of MHC-peptide binding and in identifying strong binders, SVRMHC is a promising immunoinformatics tool with not inconsiderable future potential.
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