A Compendium to Ensure Computational Reproducibility in High-Dimensional Classification Tasks

Ruschhaupt Markus, Huber Wolfgang, Poustka Annemarie and Mansmann Ulrich
Additional contact information
Ruschhaupt Markus: German Cancer Research Centre
Huber Wolfgang: German Cancer Research Center, Heidelberg, Germany
Poustka Annemarie: German Cancer Research Centre
Mansmann Ulrich: University of Heidelberg

Statistical Applications in Genetics and Molecular Biology, 2004, vol. 3, issue 1, 26

Abstract: We demonstrate a concept and implementation of a compendium for the classification of high-dimensional data from microarray gene expression profiles. A compendium is an interactive document that bundles primary data, statistical processing methods, figures, and derived data together with the textual documentation and conclusions. Interactivity allows the reader to modify and extend these components. We address the following questions: how much does the discriminatory power of a classifier depend on the choice of the algorithm that was used to identify it; what alternative classifiers could be used just as well; how robust is the result. The answers to these questions are essential prerequisites for validation and biological interpretation of the classifiers. We show how to use this approach by looking at these questions for a specific breast cancer microarray data set that first has been studied by Huang et al. (2003).

Keywords: compendium; machine learning; classification; microarray; cancer (search for similar items in EconPapers)
Date: 2004
References: Add references at CitEc
Citations: View citations in EconPapers (2)

Downloads: (external link)
https://doi.org/10.2202/1544-6115.1078 (text/html)
For access to full text, subscription to the journal or payment for the individual article is required.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:bpj:sagmbi:v:3:y:2004:i:1:n:37

Ordering information: This journal article can be ordered from
https://www.degruyter.com/journal/key/sagmb/html

DOI: 10.2202/1544-6115.1078

Access Statistics for this article

Statistical Applications in Genetics and Molecular Biology is currently edited by Michael P. H. Stumpf

More articles in Statistical Applications in Genetics and Molecular Biology from De Gruyter
Bibliographic data for series maintained by Peter Golla ().