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Covariate-Related Structure Extraction from Paired Data

  • Linfei Zhou
  • Elisabeth Georgii
  • Claudia Plant
  • Christian BöhmEmail author
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9832)

Abstract

In the biological domain, it is more and more common to apply several high-throughput technologies to the same set of samples. We propose a Covariate-Related Structure Extraction approach (CRSE) that explores relationships between different types of high-dimensional molecular data (views) in the context of sample covariate information from the experimental design, for example class membership. Real-world data analysis with an initial pipeline implementation of CRSE shows that the proposed approach successfully captures cross-view structures underlying multiple biologically relevant classification schemes, allowing to predict class labels to unseen examples from either view or across views.

Keywords

Partial Little Square Canonical Correlation Analysis Canonical Variable Covariate Information Data View 
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.

Notes

Acknowledgement

We thank Ming Jin, Jin Zhao, Basem Kanawati, Philippe Schmitt-Kopplin, Andreas Albert, J. Barbro Winkler, and Anton R. Schäffner for kindly providing the datasets used in this study.

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Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  • Linfei Zhou
    • 1
  • Elisabeth Georgii
    • 2
  • Claudia Plant
    • 3
  • Christian Böhm
    • 1
    Email author
  1. 1.Ludwig-Maximilians-Universität MünchenMunichGermany
  2. 2.Helmholtz Zentrum MünchenNeuherbergGermany
  3. 3.University of ViennaViennaAustria

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