Partitioned learning of deep Boltzmann machines for SNP data.

Journal: Bioinformatics (Oxford, England)
Published Date: Oct 15, 2017

Abstract

MOTIVATION: Learning the joint distributions of measurements, and in particular identification of an appropriate low-dimensional manifold, has been found to be a powerful ingredient of deep leaning approaches. Yet, such approaches have hardly been applied to single nucleotide polymorphism (SNP) data, probably due to the high number of features typically exceeding the number of studied individuals.

Authors

  • Moritz Hess
    Institute of Medical Biostatistics, Epidemiology and Informatics (IMBEI), University Medical Center, 55131 Mainz, Germany.
  • Stefan Lenz
    Institute of Medical Biostatistics, Epidemiology and Informatics (IMBEI), University Medical Center, 55131 Mainz, Germany.
  • Tamara J Blätte
    Department of Internal Medicine III, University Hospital of Ulm, 89081 Ulm, Germany.
  • Lars Bullinger
    Department of Internal Medicine III, University Hospital of Ulm, 89081 Ulm, Germany.
  • Harald Binder
    Institute of Medical Biostatistics, Epidemiology and Informatics (IMBEI), University Medical Center, 55131 Mainz, Germany.

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