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Peptide and major histocompatibility complex class II molecule (MHC-II) binding is the key of activating T-cell immune response. The peptides binding with MHC molecules can be well known as T-cell epitopes, and identifying epitopes is the critical for the computer-aided drug design. However, the variable lengths of binding peptides undermine the use of traditional machine learning methods. In this...
In the pathway of helper T-cell immunity, the exogenous antigen is firstly degraded into short peptides, and then some peptides can be bond to certain major histocompatibility class II (MHC-II) molecule to develop peptide-MHC complex, finally immune response is activated once receptors on helper T-cell recognize the complex. These binding peptides is named as `helper T-cell epitopes', and they can...
Recognizing linear B-cell epitopes is important for the epitope-based vaccine design, and it have been attracting worldwide researchers. Compared to traditional experimental techniques, the computational methods for epitope prediction are faster and more economical. The earliest computational methods for linear B-cell epitopes prediction were based on some amino acid property, and their performances...
The prediction of MHC-II binding peptides has long been a principal challenge in immunology. Recently, the modeling of MHC-II binding peptides has come to emphasize quantitative prediction, instead of categorizing peptides as no-binder or high-binder and moderate-binder. In this paper, we develop a support vector machine regression's (SVR) approach to predict MHC-II binding peptides. Considering global...
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