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Prediction of Protein Secondary Structures of All Types Using New Hypersphere Machine Learning Method

  • Markku Siermala
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 2101)

Abstract

In this paper, we present a new hypersphere machine learning method and use it to predict all protein secondary structures. It finds sequences with sufficiently high homology. Prediction accuracy of the new method with protein secondary structures was good (average 89.3%). However, the method could not classify all test cases.

Keywords

Secondary Structure Prediction Accuracy Secondary Structure Prediction Protein Secondary Structure Misclassification Rate 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2001

Authors and Affiliations

  • Markku Siermala
    • 1
  1. 1.Department of Computer ScienceUniversity of TampereFinland

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