Estimating the class posterior probabilities in protein secondary structure prediction
Yann Guermeur and Fabienne Thomarat
In: The 6th IAPR International Conference on Pattern Recognition in Bioinformatics, November 2-4 2011, Delft, The Netherlands.

Abstract

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.

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