کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
1165225 | 1491028 | 2013 | 6 صفحه PDF | دانلود رایگان |
![عکس صفحه اول مقاله: Using random forest to classify T-cell epitopes based on amino acid properties and molecular features Using random forest to classify T-cell epitopes based on amino acid properties and molecular features](/preview/png/1165225.png)
• An effective approach has been developed for T-cell epitopes prediction.
• A combined feature has been provided to show significant improvement in accuracy.
• Random forest provides some useful tools to select informative features and make classification simultaneously.
• A freely available web server of for predicting peptide immunogenicity is established.
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.
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Journal: Analytica Chimica Acta - Volume 804, 4 December 2013, Pages 70–75