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  Class discovery in gene expression data: characterizing splits by support vector machines

Markowetz, F., & von Heydebreck, A. (2002). Class discovery in gene expression data: characterizing splits by support vector machines. In Between Data Science And Everyday Web Practice (pp. 662-669). Berlin: Springer Verlag.

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Item Permalink: https://hdl.handle.net/11858/00-001M-0000-0010-8CBE-3 Version Permalink: https://hdl.handle.net/11858/00-001M-0000-0010-8CBF-1
Genre: Conference Paper

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 Creators:
Markowetz, Florian1, Author
von Heydebreck, Anja1, Author
Affiliations:
1Max Planck Society, ou_persistent13              

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 Abstract: We present a variation of ISIS, a class discovery method for microarray data described by Heydebreck et al. (2001). The objective is to discover biologically relevant structures in the gene expression profiles of different tissue samples in an unsupervised fashion. The method searches for binary partitions in the set of samples that show clear separation. Mathematically, each class distinction is characterized according to the size of margin achieved by a support vector machine (svm) separating the two classes. The method produces not only one partition (like most commonly used clustering algorithms) but several mutually independent ones. The significance of the margin as a measure of class distinction is shown by comparison to random partitions of the samples. In three data sets from cancer gene expression studies the svm-margin approach succeeds in detecting relationships between the tissue samples, for example cancer subtypes. The known biological classes exhibit a exceptionally large value of the svm-margin. We compare the outcome of the svm-margin method to a characterization of bipartitions of the samples based on Diagonal Linear Discriminant Analysis.

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Language(s): eng - English
 Dates: 2002
 Publication Status: Issued
 Pages: -
 Publishing info: -
 Table of Contents: -
 Rev. Type: -
 Identifiers: eDoc: 29178
 Degree: -

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Title: 26th Annual Conference of the Gesellschaft für Klassifikation
Place of Event: University of Mannheim
Start-/End Date: 2002-07-22 - 2002-07-24

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Title: Between Data Science And Everyday Web Practice
Source Genre: Proceedings
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Affiliations:
Publ. Info: Berlin : Springer Verlag
Pages: - Volume / Issue: (/) Sequence Number: - Start / End Page: 662 - 669 Identifier: -

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Title: Studies in Classification, Data Analysis, and Knowledge
Source Genre: Series
 Creator(s):
Schader, Martin, Editor
Gaul, Wolfgang, Editor
Vichi, Maurizio, Editor
Affiliations:
-
Publ. Info: -
Pages: - Volume / Issue: (/) Sequence Number: - Start / End Page: - Identifier: -