ArticleFrontiers in immunology2018
iBCE-EL: A New Ensemble Learning Framework for Improved Linear B-Cell Epitope Prediction.
Article in Frontiers in immunology, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 78 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
78 citing papers in PubMed, 174 citations in OpenAlex.
- A novel multi-epitope mRNA vaccine against Escherichia coli using an in silico reverse vaccinology approach.Journal of applied genetics · 2026Article
- The critical role of artificial intelligence and bioinformatics in accelerating peptide-based vaccine discovery for tackling global infectious diseases.Briefings in bioinformatics · 2026Review
- Designing of a conserved subunit multiepitope vaccine candidate against Francisella tularensis Schu S4 using immunoinformatics.Scientific reports · 2026Article
- Improving B-cell Linear Epitope PredictionCurrent drug targets · 2026Article
- The impact of biologically relevant negative samples on machine learning-based B-cell epitope prediction for Influenza A.Bioinformatics advances · 2026Article
- BLMPred: Predicting linear B-cell epitopes using pre-trained protein language models and machine learning.Computational and structural biotechnology journal · 2026Article
- Immunoinformatic Prediction of HIV-1 Glycoprotein gp120 and Nef Epitopes Conjugated to HBsAg-Binding Protein (SBP) to Induce the Humoral and Cellular Immune Response.International journal of molecular sciences · 2025Article
- Computational Methods in Immunoinformatics: Epitope Discovery and Diagnostic Applications.ACS omega · 2025Review
- Transformer-based deep learning enables improved B-cell epitope prediction in parasitic pathogens: A proof-of-concept study on Fasciola hepatica.PLoS neglected tropical diseases · 2025Article
- Interpretable molecular encodings and representations for machine learning tasks.Computational and structural biotechnology journal · 2024Article
- Advances of computational methods enhance the development of multi-epitope vaccines.Briefings in bioinformatics · 2024Review
- Multi-epitope vaccine design of African swine fever virus considering T cell and B cell immunogenicity.AMB Express · 2024Article
- BiSpec Pairwise AI: guiding the selection of bispecific antibody target combinations with pairwise learning and GPT augmentation.Journal of cancer research and clinical oncology · 2024Article
- Immunoinformatic Identification of Multiple Epitopes of gp120 Protein of HIV-1 to Enhance the Immune Response against HIV-1 Infection.International journal of molecular sciences · 2024Article
- Prediction of linear B-cell epitopes based on protein sequence features and BERT embeddings.Scientific reports · 2024Article
- Optimizing sheep B-cell epitopes inFrontiers in immunology · 2024Article
- Multi-perspectives and challenges in identifying B-cell epitopes.Protein science : a publication of the Protein Society · 2023Review
- Recent Progress in Antibody Epitope Prediction.Antibodies (Basel, Switzerland) · 2023Review
- epitope1D: accurate taxonomy-aware B-cell linear epitope prediction.Briefings in bioinformatics · 2023Article
- Monoclonal antibody targeting a novel linear epitope on nucleoprotein confers pan-reactivity to influenza A virus.Applied microbiology and biotechnology · 2023Article
18 more citing papers are in PubMed but not listed here.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors at 3 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Identification of B-cell epitopes (BCEs) is a fundamental step for epitope-based vaccine development, antibody production, and disease prevention and diagnosis. Due to the avalanche of protein sequence data discovered in postgenomic age, it is essential to develop an automated computational method to enable fast and accurate identification of novel BCEs within vast number of candidate proteins and peptides. Although several computational methods have been developed, their accuracy is unreliable. Thus, developing a reliable model with significant prediction improvements is highly desirable. In this study, we first constructed a non-redundant data set of 5,550 experimentally validated BCEs and 6,893 non-BCEs from the Immune Epitope Database. We then developed a novel ensemble learning framework for improved linear BCE predictor called iBCE-EL, a fusion of two independent predictors, namely, extremely randomized tree (ERT) and gradient boosting (GB) classifiers, which, respectively, uses a combination of physicochemical properties (PCP) and amino acid composition and a combination of dipeptide and PCP as input features. Cross-validation analysis on a benchmarking data set showed that iBCE-EL performed better than individual classifiers (ERT and GB), with a Matthews correlation coefficient (MCC) of 0.454. Furthermore, we evaluated the performance of iBCE-EL on the independent data set. Results show that iBCE-EL significantly outperformed the state-of-the-art method with an MCC of 0.463. To the best of our knowledge, iBCE-EL is the first ensemble method for linear BCEs prediction. iBCE-EL was implemented in a web-based platform, which is available at http://thegleelab.org/iBCE-EL. iBCE-EL contains two prediction modes. The first one identifying peptide sequences as BCEs or non-BCEs, while later one is aimed at providing users with the option of mining potential BCEs from protein sequences.
Indexed as
Identifiers
What OpenQuestion holds
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.