title: Building MHC class II epitope predictor using machine learning approaches.
abstract: Identification of T-cell epitopes binding to MHC class II molecules is an important step in epitope-based vaccine development. This process has since been accelerated with the use of bioinformatics tools to aid in the prediction of peptide binding to MHC class II molecules and also to systematically scan for candidate peptides in antigenic proteins. There have been many prediction software developed over the years using various methods and algorithms and they are becoming increasingly sophisticated. Here, we illustrate the use of machine learning algorithms to train on MHC class II peptide data represented by feature vectors describing their amino acid physicochemical properties. The developed prediction model can then be used to predict new peptide data.
authors.affiliation: Department of Biochemistry, National University of Singapore, 14 Medical Drive #14-01T, Singapore, Singapore, 117599.
authors.forename: Loan Ping
authors.initials: LP
authors.lastname: Eng
authors.forename: Tin Wee
authors.initials: TW
authors.lastname: Tan
authors.forename: Joo Chuan
authors.initials: JC
authors.lastname: Tong
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D019295:Computational Biology / Q000379:methods; D018984:Epitopes, T-Lymphocyte / Q000737:chemistry / Q000378:metabolism*; D000949:Histocompatibility Antigens Class II / Q000737:chemistry / Q000378:metabolism*; D006801:Humans; D008958:Models, Molecular; D012984:Software
chemical_list: D018984:Epitopes, T-Lymphocyte; D000949:Histocompatibility Antigens Class II