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Covariate-Related Structure Extraction from Paired Data

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Information Technology in Bio- and Medical Informatics (ITBAM 2016)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 9832))

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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.

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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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Authors and Affiliations

  1. Ludwig-Maximilians-Universität München, Munich, Germany

    Linfei Zhou & Christian Böhm

  2. Helmholtz Zentrum München, Neuherberg, Germany

    Elisabeth Georgii

  3. University of Vienna, Vienna, Austria

    Claudia Plant

Authors
  1. Linfei Zhou
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  2. Elisabeth Georgii
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  3. Claudia Plant
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  4. Christian Böhm
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Corresponding author

Correspondence to Christian Böhm .

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Editors and Affiliations

  1. Institute of Informatics and Telematics , Pisa, Italy

    M. Elena Renda

  2. Czech Technical University in Prague , Prague 6, Czech Republic

    Miroslav Bursa

  3. Medical University Graz , Graz, Austria

    Andreas Holzinger

  4. San José State University , San Jose, California, USA

    Sami Khuri

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Zhou, L., Georgii, E., Plant, C., Böhm, C. (2016). Covariate-Related Structure Extraction from Paired Data. In: Renda, M., Bursa, M., Holzinger, A., Khuri, S. (eds) Information Technology in Bio- and Medical Informatics. ITBAM 2016. Lecture Notes in Computer Science(), vol 9832. Springer, Cham. https://doi.org/10.1007/978-3-319-43949-5_11

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  • DOI: https://doi.org/10.1007/978-3-319-43949-5_11

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-43948-8

  • Online ISBN: 978-3-319-43949-5

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