title: Using random forest to classify T-cell epitopes based on amino acid properties and molecular features.
abstract: T-lymphocyte (T-cell) is a very important component in human immune system. T-cell epitopes can be used for the accurately monitoring the immune responses which activation by major histocompatibility complex (MHC), and rationally designing vaccines. Therefore, accurate prediction of T-cell epitopes is crucial for vaccine development and clinical immunology. In current study, two types peptide features, i.e., amino acid properties and chemical molecular features were used for the T-cell epitopes peptide representation. Based on these features, random forest (RF) algorithm, a powerful machine learning algorithm, was used to classify T-cell epitopes and non-T-cell epitopes. The classification accuracy, sensitivity, specificity, Matthews correlation coefficient (MCC), and area under the curve (AUC) values for proposed method are 97.54%, 97.22%, 97.60%, 0.9193, and 0.9868, respectively. These results indicate that current method based on the combined features and RF is effective for T-cell epitopes prediction.
authors.affiliation: Research Center of Modernization of Traditional Chinese Medicines, Central South University, Changsha 410083, PR China.
authors.forename: Jian-Hua
authors.initials: JH
authors.lastname: Huang
authors.forename: Hua-Lin
authors.initials: HL
authors.lastname: Xie
authors.forename: Jun
authors.initials: J
authors.lastname: Yan
authors.forename: Hong-Mei
authors.initials: HM
authors.lastname: Lu
authors.forename: Qing-Song
authors.initials: QS
authors.lastname: Xu
authors.forename: Yi-Zeng
authors.initials: YZ
authors.lastname: Liang
mesh_terms: D000596:Amino Acids / Q000737:chemistry*; D000939:Epitopes / Q000737:chemistry*; D020407:Internet; D012372:ROC Curve; D013601:T-Lymphocytes / Q000737:chemistry*
chemical_list: D000596:Amino Acids; D000939:Epitopes
keywords: Amino acid properties; Chemical molecular features; MHC; RF; Random forest (RF); T cell receptors; T-cell epitopes; TCRs; major histocompatibility complex; random forest