ArticleJournal of biomedicine & biotechnology2011
Prediction of B-cell linear epitopes with a combination of support vector machine classification and amino acid propensity identification.
Article in Journal of biomedicine & biotechnology, 2011. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 39 papers.
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Who cites it
39 citing papers in PubMed, 90 citations in OpenAlex.
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- AI-powered mapping of tumor immunity for optimized mRNA vaccine engineering.Frontiers in oncology · 2026Review
- B-EPIC: A Transformer-Based Language Model for Decoding B Cell Immunodominance Patterns.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2025Article
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- Multi-perspectives and challenges in identifying B-cell epitopes.Protein science : a publication of the Protein Society · 2023Review
- Evaluation of immunodominant peptides of in vivo expressed mycobacterial antigens in an ELISA-based diagnostic assay for pulmonary tuberculosis.Brazilian journal of microbiology : [publication of the Brazilian Society for Microbiology] · 2023Article
- In-silico identification of linear B-cell epitopes in specific proteins of Bartonella bacilliformis for the serological diagnosis of Carrion's disease.PLoS neglected tropical diseases · 2023Article
- Prediction of HIV sensitivity to monoclonal antibodies using aminoacid sequences and deep learning.Bioinformatics (Oxford, England) · 2022Article
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- Does immune recognition of SARS-CoV2 epitopes vary between different ethnic groups?Virus research · 2021Article
- Conformational epitope matching and prediction based on protein surface spiral features.BMC genomics · 2021Article
- Linear B-Cell Epitope Prediction for In Silico Vaccine Design: A Performance Review of Methods Available via Command-Line Interface.International journal of molecular sciences · 2021Review
- Disruption of VirB6 Paralogs in Anaplasma phagocytophilum Attenuates Its Growth.Journal of bacteriology · 2020Article
- Deep learning methods improve linear B-cell epitope prediction.BioData mining · 2020Article
- Laboratory Diagnosis of Human Brucellosis.Clinical microbiology reviews · 2019Review
- PEPOP 2.0: new approaches to mimic non-continuous epitopes.BMC bioinformatics · 2019Article
- A voting mechanism-based linear epitope prediction system for the host-specific Iridoviridae family.BMC bioinformatics · 2019Article
- Targeting Tumor Markers with Antisense Peptides: An Example of Human Prostate Specific Antigen.International journal of molecular sciences · 2019Article
- Non-destructive detection of matrix stabilization correlates with enhanced mechanical properties of self-assembled articular cartilage.Journal of tissue engineering and regenerative medicine · 2019Article
Corrections and comments
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Authors and funding
4 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Epitopes are antigenic determinants that are useful because they induce B-cell antibody production and stimulate T-cell activation. Bioinformatics can enable rapid, efficient prediction of potential epitopes. Here, we designed a novel B-cell linear epitope prediction system called LEPS, Linear Epitope Prediction by Propensities and Support Vector Machine, that combined physico-chemical propensity identification and support vector machine (SVM) classification. We tested the LEPS on four datasets: AntiJen, HIV, a newly generated PC, and AHP, a combination of these three datasets. Peptides with globally or locally high physicochemical propensities were first identified as primitive linear epitope (LE) candidates. Then, candidates were classified with the SVM based on the unique features of amino acid segments. This reduced the number of predicted epitopes and enhanced the positive prediction value (PPV). Compared to four other well-known LE prediction systems, the LEPS achieved the highest accuracy (72.52%), specificity (84.22%), PPV (32.07%), and Matthews' correlation coefficient (10.36%).
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Registered trials
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.