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Supervised Selective Kernel Fusion for Membrane Protein Prediction

  • Alexander Tatarchuk
  • Valentina Sulimova
  • Ivan Torshin
  • Vadim Mottl
  • David Windridge
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8626)

Abstract

Membrane protein prediction is a significant classification problem, requiring the integration of data derived from different sources such as protein sequences, gene expression, protein interactions etc. A generalized probabilistic approach for combining different data sources via supervised selective kernel fusion was proposed in our previous papers. It includes, as particular cases, SVM, Lasso SVM, Elastic Net SVM and others. In this paper we apply a further instantiation of this approach, the Supervised Selective Support Kernel SVM and demonstrate that the proposed approach achieves the top-rank position among the selective kernel fusion variants on benchmark data for membrane protein prediction. The method differs from the previous approaches in that it naturally derives a subset of “support kernels” (analogous to support objects within SVMs), thereby allowing the memory-efficient exclusion of significant numbers of irrelevant kernel matrixes from a decision rule in a manner particularly suited to membrane protein prediction.

Keywords

Multiple Kernel Learning SVM supervised selectivity support kernels membrane protein prediction 

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Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Alexander Tatarchuk
    • 1
  • Valentina Sulimova
    • 2
  • Ivan Torshin
    • 1
  • Vadim Mottl
    • 1
  • David Windridge
    • 3
  1. 1.Computing Center of the Russian Academy of SciencesMoscowRussia
  2. 2.Tula State UniversityTulaRussia
  3. 3.Centre for Vision, Speech and Signal ProcessingUniversity of SurreyGuildfordUK

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