Building MHC class II epitope predictor using machine learning approaches.

Clicks: 313
ID: 100979
2015
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Ranked #50 of 139 articles by views in methods in molecular biology (clifton, nj)

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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.
Reference Key
eng2015buildingmethods Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Eng, Loan Ping;Tan, Tin Wee;Tong, Joo Chuan;
Journal methods in molecular biology (clifton, nj)
Year 2015
DOI
10.1007/978-1-4939-2285-7_4
URL
Keywords
Monoclonal antibody cdr grafting antibody humanization antibody variable domain orientation

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