Skip to main content
SpringerLink
Log in
Book cover

International Symposium on Medical Data Analysis

ISMDA 2000: Medical Data Analysis pp 300–307Cite as

Selection of Informative Genes in Gene Expression Based Diagnosis: A Nonparametric Approach

  • Conference paper
  • First Online:

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 1933))

Abstract

We study a nonparametric approach to cancer classification using data from DNA microarrays. We compare our approach to the approach of [4].

This is a preview of subscription content, log in via an institution.

Chapter
USD   29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD   39.99
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD   54.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Learn about institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Alizadeh, A. A. et al: Distinct types of diffuse large B-cell lymphoma identified by gene expression profiling. Nature 403 (2000) 503–511

    Article  Google Scholar 

  2. Ciampi, A. et al: Recursive partioning: a versatile method for exploratory data analysis in Biostatistics. in Biostatistics (eds. I. B. MacNeill and G. J. Umphrey). (1987) D. Reidel Publishing, New York

    Google Scholar 

  3. Gail, M. H. and Green, S. B.: A generalization of the one-sided two-sample Kolmogorov-Smirnov statistic for evaluating diagnostic tests. Biometrics 32 (1976) 561–570

    Google Scholar 

  4. Golub, T. R. et al: Molecular classification of cancer: class discovery and class prediction by gene expression monitoring. Science 286 (1999) 531–536

    Article  Google Scholar 

  5. Miller, R. and Siegmund, D.: Maximally selected chi-square statistics. Biometrics38 (1982) 1011–1016

    Article  MATH  MathSciNet  Google Scholar 

  6. Slonim, D. K. et al: Class prediction and discovery using gene expression data. Manuscript available at http://www.genome.wi.mit.edu/MPR

Download references

Author information

Authors and Affiliations

  1. Universität Freiburg, Institut für Mathematische Stochastik, Eckerstr. 1, D-79104, Freiburg, Germany

    Martin Beibel

Authors
  1. Martin Beibel
    View author publications

    You can also search for this author in PubMed Google Scholar

Editor information

Editors and Affiliations

  1. Fachbereich Biologie und Informatik AG Adaptive Systemarchitektur, Johann Wolfgang Goethe-Universität, Robert-Mayer-Str. 11-15, 60054, Frankfurt a.M., Germany

    Rüdiger W. Brause

  2. Klinik für Allgemein- und Gefässchirurgie, Johann Wolfgang Goethe-Universität, Theodor-Stern-Kai 7, 60590, Frankfurt a.M., Germany

    Ernst Hanisch

Rights and permissions

Reprints and permissions

Copyright information

© 2000 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Beibel, M. (2000). Selection of Informative Genes in Gene Expression Based Diagnosis: A Nonparametric Approach. In: Brause, R.W., Hanisch, E. (eds) Medical Data Analysis. ISMDA 2000. Lecture Notes in Computer Science, vol 1933. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-39949-6_36

Download citation

  • DOI: https://doi.org/10.1007/3-540-39949-6_36

  • Published:

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-41089-8

  • Online ISBN: 978-3-540-39949-0

  • eBook Packages: Springer Book Archive

Publish with us

Policies and ethics

Search

Navigation