Prediction of MHC Class I Binding Peptides Using Fourier Analysis and Support Vector Machine

  • Feng Shi
  • Qiujian Chen
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4223)


Processing and presentation of major histocompatibility complex class I antigens to cytotoxic T-lymphocytes is crucial for immune surveillance against intracellular bacteria, parasites, viruses and tumors. Identification of antigenic regions on pathogen proteins will play a pivotal role in designer vaccine immunotherapy. We have developed a novel method that identifies MHC class I binding peptides from peptides sequences. For the first time we present a method for MHC class I binding peptides prediction using Fourier analysis and support vector machines (SVM). Using cross-validation, we demonstrate that this novel prediction technique has a reasonable performance.


Support Vector Machine Major Histocompatibility Complex Class Binding Peptide Support Vector Machine Training Anchor Residue 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Feng Shi
    • 1
    • 2
  • Qiujian Chen
    • 1
  1. 1.College of ScienceHuazhong Agricultural UniversityWuhanP.R. China
  2. 2.School of Mathematics and StatisticsWuhan UniversityWuhanP.R. China

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