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• W4243422210 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." @default.
• W4243422210 created "2022-05-12" @default.
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• W4243422210 date "2013-12-01" @default.
• W4243422210 modified "2023-10-18" @default.
• W4243422210 title "Using random forest to classify T-cell epitopes based on amino acid properties and molecular features" @default.
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• W4243422210 doi "https://doi.org/10.1016/j.aca.2013.10.003" @default.
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