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A Class of New Kernels Based on High-Scored Pairs of k-Peptides for SVMs and Its Application for Prediction of Protein Subcellular Localization

  • Zhengdeng Lei
  • Yang Dai
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3680)

Abstract

A class of new kernels has been developed for vectors derived from a coding scheme of the k-peptide composition for protein sequences. Each kernel defines the biological similarity for two mapped k-peptide coding vectors. The mapping transforms a k-peptide coding vector into a new vector based on a matrix formed by high BLOSUM scores associated with pairs of k-peptides. In conjunction with the use of support vector machines, the effectiveness of the new kernels is evaluated against the conventional coding scheme of k-peptide (k ≤ 3) for the prediction of subcellular localizations of proteins in Gram-negative bacteria. It is demonstrated that the new method outperforms all the other methods in a 5-fold cross-validation.

Keywords

Protein subcellular localization BLOSUM matrix kernel support vector machine Gram-negative bacteria 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2005

Authors and Affiliations

  • Zhengdeng Lei
    • 1
  • Yang Dai
    • 1
  1. 1.Department of Bioengineering (MC063)University of Illinois at ChicagoChicagoUSA

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