Estimating the Class Posterior Probabilities in Protein Secondary Structure Prediction

  • Yann Guermeur
  • Fabienne Thomarat
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7036)


Support vector machines, let them be bi-class or multi-class, have proved efficient for protein secondary structure prediction. They can be used either as sequence-to-structure classifier, structure-to-structure classifier, or both. Compared to the classifier most commonly found in the main prediction methods, the multi-layer perceptron, they exhibit one single drawback: their outputs are not class posterior probability estimates. This paper addresses the problem of post-processing the outputs of multi-class support vector machines used as sequence-to-structure classifiers with a structure-to-structure classifier estimating the class posterior probabilities. The aim of this comparative study is to obtain improved performance with respect to both criteria: prediction accuracy and quality of the estimates.


protein secondary structure prediction multi-class support vector machines class membership probabilities 


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Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Yann Guermeur
    • 1
  • Fabienne Thomarat
    • 1
  1. 1.LORIA – Equipe ABCVandœuvre-lès-NancyFrance

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