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  Molecular decomposition of complex clinical phenotypes using biologically structured analysis of microarray data

Lottaz, C., & Spang, R. (2005). Molecular decomposition of complex clinical phenotypes using biologically structured analysis of microarray data. Bioinformatics, 21(9), 1971-1978. doi:10.1093/bioinformatics/bti292.

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Item Permalink: http://hdl.handle.net/11858/00-001M-0000-0010-870B-0 Version Permalink: http://hdl.handle.net/11858/00-001M-0000-0010-870C-E
Genre: Journal Article

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1971.pdf (Any fulltext), 365KB
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 Creators:
Lottaz, Claudio1, Author
Spang, Rainer2, Author              
Affiliations:
1Max Planck Society, ou_persistent13              
2Dept. of Computational Molecular Biology (Head: Martin Vingron), Max Planck Institute for Molecular Genetics, Max Planck Society, ou_1433547              

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 Abstract: Motivation: Today, the characterization of clinical phenotypes by gene-expression patterns is widely used in clinical research. If the investigated phenotype is complex from the molecular point of view, new challanges arise and these have not been adressed systematically. For instance, the same clinical phenotype can be caused by various molecular disorders, such that one observes different characteristic expression patterns in different patients. Results: In this paper we describe a novel algorithm called Structured Analysis of Microarrays (StAM), which accounts for molecular heterogeneity of complex clinical phenotypes. Our algorithm goes beyond established methodology in several aspects: in addition to the expression data, it exploits functional annotations from the Gene Ontology database to build biologically focussed classifiers. These are used to uncover potential molecular disease subentities and associate them to biological processes without compromising overall prediction accuracy.

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Language(s): eng - English
 Dates: 2005-01-27
 Publication Status: Published in print
 Pages: -
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 Table of Contents: -
 Rev. Type: -
 Identifiers: eDoc: 265321
DOI: 10.1093/bioinformatics/bti292
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Title: Bioinformatics
Source Genre: Journal
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Pages: - Volume / Issue: 21 (9) Sequence Number: - Start / End Page: 1971 - 1978 Identifier: ISSN: 1367-4803