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Predictive Vaccinology: Optimisation of Predictions Using Support Vector Machine Classifiers

Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3578)

Abstract

Promiscuous human leukocyte antigen (HLA) binding peptides are ideal targets for vaccine development. Existing computational models for prediction of promiscuous peptides used hidden Markov models and artificial neural networks as prediction algorithms. We report a system based on support vector machines that outperforms previously published methods. Preliminary testing showed that it can predict peptides binding to HLA-A2 and -A3 supertype molecules with excellent accuracy, even for molecules where no binding data are currently available.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2005

Authors and Affiliations

  1. 1.Faculty of MathematicsUniversity of BelgradeBelgradeSerbia
  2. 2.Institute for Infocomm ResearchSingapore
  3. 3.School of Computer EngineeringNanyang Technological UniversitySingapore
  4. 4.School of Land and Food Sciences and the Institute for Molecular BioscienceUniversity of QueenslandBrisbaneAustralia

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