ArticleGenomics, proteomics & bioinformatics2020
iLBE for Computational Identification of Linear B-cell Epitopes by Integrating Sequence and Evolutionary Features.
Article in Genomics, proteomics & bioinformatics, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 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
20 citing papers in PubMed, 38 citations in OpenAlex.
- Computational identification of B- and T-cell epitopes: a unified task taxonomy and review of databases, datasets, predictive pipelines, and gaps.Frontiers in immunology · 2026Review
- Computational Methods in Immunoinformatics: Epitope Discovery and Diagnostic Applications.ACS omega · 2025Review
- Prediction of linear B-cell epitopes based on protein sequence features and BERT embeddings.Scientific reports · 2024Article
- DeepLBCEPred: A Bi-LSTM and multi-scale CNN-based deep learning method for predicting linear B-cell epitopes.Frontiers in microbiology · 2023Article
- NetBCE: An Interpretable Deep Neural Network for Accurate Prediction of Linear B-cell Epitopes.Genomics, proteomics & bioinformatics · 2022Article
- Prediction of B cell epitopes in proteins using a novel sequence similarity-based method.Scientific reports · 2022Article
- Quantitative In Silico Evaluation of Allergenic Proteins fromCurrent issues in molecular biology · 2022Article
- Hierarchical representation for PPI sites prediction.BMC bioinformatics · 2022Article
- Prediction of serine phosphorylation sites mapping on Schizosaccharomyces Pombe by fusing three encoding schemes with the random forest classifier.Scientific reports · 2022Article
- Identifying Pupylation Proteins and Sites by Incorporating Multiple Methods.Frontiers in endocrinology · 2022Article
- SEMA: Antigen B-cell conformational epitope prediction using deep transfer learning.Frontiers in immunology · 2022Article
- Advances in antibody discovery from human BCR repertoires.Frontiers in bioinformatics · 2022Review
- Network-based transcriptomic analysis identifies the genetic effect of COVID-19 to chronic kidney disease patients: A bioinformatics approach.Saudi journal of biological sciences · 2021Article
- TUPDB: Target-Unrelated Peptide Data Bank.Interdisciplinary sciences, computational life sciences · 2021Article
- Prediction of functional outcomes of schizophrenia with genetic biomarkers using a bagging ensemble machine learning method with feature selection.Scientific reports · 2021Article
- PredNTS: Improved and Robust Prediction of Nitrotyrosine Sites by Integrating Multiple Sequence Features.International journal of molecular sciences · 2021Article
- IRC-Fuse: improved and robust prediction of redox-sensitive cysteine by fusing of multiple feature representations.Journal of computer-aided molecular design · 2021Article
- PUP-Fuse: Prediction of Protein Pupylation Sites by Integrating Multiple Sequence Representations.International journal of molecular sciences · 2021Article
- Improved prediction and characterization of anticancer activities of peptides using a novel flexible scoring card method.Scientific reports · 2021Article
- Antibody Class(es) Predictor for Epitopes (AbCPE): A Multi-Label Classification Algorithm.Frontiers in bioinformatics · 2021Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Linear B-cell epitopes are critically important for immunological applications, such as vaccine design, immunodiagnostic test, and antibody production, as well as disease diagnosis and therapy. The accurate identification of linear B-cell epitopes remains challenging despite several decades of research. In this work, we have developed a novel predictor, Identification of Linear B-cell Epitope (iLBE), by integrating evolutionary and sequence-based features. The successive feature vectors were optimized by a Wilcoxon-rank sum test. Then the random forest (RF) algorithm using the optimal consecutive feature vectors was applied to predict linear B-cell epitopes. We combined the RF scores by the logistic regression to enhance the prediction accuracy. iLBE yielded an area under curve score of 0.809 on the training dataset and outperformed other prediction models on a comprehensive independent dataset. iLBE is a powerful computational tool to identify the linear B-cell epitopes and would help to develop penetrating diagnostic tests. A web application with curated datasets for iLBE is freely accessible at http://kurata14.bio.kyutech.ac.jp/iLBE/.
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.