ArticlePloS one2015
Accurate prediction of immunogenic T-cell epitopes from epitope sequences using the genetic algorithm-based ensemble learning.
Article in PloS one, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 22 papers.
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22 citing papers in PubMed, 35 citations in OpenAlex.
- Deep learning of antibody epitopes using positional permutation vectors.Computational and structural biotechnology journal · 2024Article
- Genetic Risk Assessment of Nonsyndromic Cleft Lip with or without Cleft Palate by Linking Genetic Networks and Deep Learning Models.International journal of molecular sciences · 2023Article
- 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
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- iNuc-ext-PseTNC: an efficient ensemble model for identification of nucleosome positioning by extending the concept of Chou's PseAAC to pseudo-tri-nucleotide composition.Molecular genetics and genomics : MGG · 2019Article
- Systems Chemical Genetics-Based Drug Discovery: Prioritizing Agents Targeting Multiple/Reliable Disease-Associated Genes as Drug Candidates.Frontiers in genetics · 2019Article
- Sequence-based bacterial small RNAs prediction using ensemble learning strategies.BMC bioinformatics · 2018Article
- SFPEL-LPI: Sequence-based feature projection ensemble learning for predicting LncRNA-protein interactions.PLoS computational biology · 2018Article
- Prediction of high anti-angiogenic activity peptides in silico using a generalized linear model and feature selection.Scientific reports · 2018Article
- M6APred-EL: A Sequence-Based Predictor for Identifying N6-methyladenosine Sites Using Ensemble Learning.Molecular therapy. Nucleic acids · 2018Article
- RFAmyloid: A Web Server for Predicting Amyloid Proteins.International journal of molecular sciences · 2018Article
- Predicting drug-disease associations by using similarity constrained matrix factorization.BMC bioinformatics · 2018Article
- Sc-ncDNAPred: A Sequence-Based Predictor for Identifying Non-coding DNA inFrontiers in microbiology · 2018Article
- Identifying Plant Pentatricopeptide Repeat Coding Gene/Protein Using Mixed Feature Extraction Methods.Frontiers in plant science · 2018Article
- Drug-Target Interaction Prediction through Label Propagation with Linear Neighborhood Information.Molecules (Basel, Switzerland) · 2017Article
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- Analysis and prediction of single-stranded and double-stranded DNA binding proteins based on protein sequences.BMC bioinformatics · 2017Article
- A genetic algorithm-based weighted ensemble method for predicting transposon-derived piRNAs.BMC bioinformatics · 2016Article
- Designing string-of-beads vaccines with optimal spacers.Genome medicine · 2016Article
Corrections and comments
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Authors and funding
6 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundT-cell epitopes play the important role in T-cell immune response, and they are critical components in the epitope-based vaccine design. Immunogenicity is the ability to trigger an immune response. The accurate prediction of immunogenic T-cell epitopes is significant for designing useful vaccines and understanding the immune system.
methodsIn this paper, we attempt to differentiate immunogenic epitopes from non-immunogenic epitopes based on their primary structures. First of all, we explore a variety of sequence-derived features, and analyze their relationship with epitope immunogenicity. To effectively utilize various features, a genetic algorithm (GA)-based ensemble method is proposed to determine the optimal feature subset and develop the high-accuracy ensemble model. In the GA optimization, a chromosome is to represent a feature subset in the search space. For each feature subset, the selected features are utilized to construct the base predictors, and an ensemble model is developed by taking the average of outputs from base predictors. The objective of GA is to search for the optimal feature subset, which leads to the ensemble model with the best cross validation AUC (area under ROC curve) on the training set.
resultsTwo datasets named 'IMMA2' and 'PAAQD' are adopted as the benchmark datasets. Compared with the state-of-the-art methods POPI, POPISK, PAAQD and our previous method, the GA-based ensemble method produces much better performances, achieving the AUC score of 0.846 on IMMA2 dataset and the AUC score of 0.829 on PAAQD dataset. The statistical analysis demonstrates the performance improvements of GA-based ensemble method are statistically significant.
conclusionsThe proposed method is a promising tool for predicting the immunogenic epitopes. The source codes and datasets are available in S1 File.
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