title: Accurate prediction of immunogenic T-cell epitopes from epitope sequences using the genetic algorithm-based ensemble learning.
abstract: BACKGROUND
  T-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.
  
  
  METHODS
  In 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.
  
  
  RESULTS
  Two 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.
  
  
  CONCLUSIONS
  The proposed method is a promising tool for predicting the immunogenic epitopes. The source codes and datasets are available in S1 File.
authors.affiliation: School of Computer, Wuhan University, Wuhan, 430072, China; Research Institute of Shenzhen, Wuhan University, Shenzhen, 518057, China.
authors.forename: Wen
authors.initials: W
authors.lastname: Zhang
authors.affiliation: School of Mathematics and Statistics, South-central University for Nationalities, Wuhan, 430074, China.
authors.forename: Yanqing
authors.initials: Y
authors.lastname: Niu
authors.affiliation: School of Computer, Wuhan University, Wuhan, 430072, China.
authors.forename: Hua
authors.initials: H
authors.lastname: Zou
authors.affiliation: School of Mathematics and Statistics, Wuhan University, Wuhan, 430072, China.
authors.forename: Longqiang
authors.initials: L
authors.lastname: Luo
authors.affiliation: School of Computer, Wuhan University, Wuhan, 430072, China.
authors.forename: Qianchao
authors.initials: Q
authors.lastname: Liu
authors.affiliation: School of Computer, Wuhan University, Wuhan, 430072, China.
authors.forename: Weijian
authors.initials: W
authors.lastname: Wu
mesh_terms: D000465:Algorithms; D000595:Amino Acid Sequence; D003198:Computer Simulation; D066264:Datasets as Topic; D018984:Epitopes, T-Lymphocyte / Q000737:chemistry* / Q000276:immunology; D006801:Humans; D008957:Models, Genetic; D018448:Models, Immunological; D008969:Molecular Sequence Data; D012372:ROC Curve; D013601:T-Lymphocytes / Q000737:chemistry / Q000276:immunology; D014614:Vaccines, Synthetic / Q000096:biosynthesis
chemical_list: D018984:Epitopes, T-Lymphocyte; D014614:Vaccines, Synthetic