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Gene selection in arthritis classification with large-scale microarray expression profiles

The use of large‐scale microarray expression profiling to identify predictors of disease class has become of major interest. Beyond their impact in the clinical setting (i.e. improving diagnosis and treatment), these markers are also likely to provide clues on the molecular mechanisms underlining th... Full description

Journal Title: Comparative and Functional Genomics 2003, Vol.4 (4), p.171-181
Main Author: Sha, Naijun
Other Authors: Vannucci, Marina , Brown, Philip J. , Trower, Michael K. , Amphlett, Gillian , Falciani, Francesco
Format: Electronic Article Electronic Article
Language: English
Subjects:
Quelle: Alma/SFX Local Collection
Publisher: Chichester, UK: John Wiley & Sons, Ltd
ID: ISSN: 0749-503X
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recordid: cdi_pubmedcentral_primary_oai_pubmedcentral_nih_gov_2447416
title: Gene selection in arthritis classification with large-scale microarray expression profiles
format: Article
creator:
  • Sha, Naijun
  • Vannucci, Marina
  • Brown, Philip J.
  • Trower, Michael K.
  • Amphlett, Gillian
  • Falciani, Francesco
subjects:
  • Article Subject
  • Bayesian variable selection
  • Biological and medical sciences
  • Biology (General)
  • classification
  • Fundamental and applied biological sciences. Psychology
  • gene expression profiling
  • General aspects
  • Genetics
  • Mathematics in biology. Statistical analysis. Models. Metrology. Data processing in biology (general aspects)
  • Research Article
  • Science
ispartof: Comparative and Functional Genomics, 2003, Vol.4 (4), p.171-181
description: The use of large‐scale microarray expression profiling to identify predictors of disease class has become of major interest. Beyond their impact in the clinical setting (i.e. improving diagnosis and treatment), these markers are also likely to provide clues on the molecular mechanisms underlining the diseases. In this paper we describe a new method for the identification of multiple gene predictors of disease class. The method is applied to the classification of two forms of arthritis that have a similar clinical endpoint but different underlying molecular mechanisms: rheumatoid arthritis (RA) and osteoarthritis (OA). We aim at both the classification of samples and the location of genes characterizing the different classes. We achieve both goals simultaneously by combining a binary probit model for classification with Bayesian variable selection methods to identify important genes. We find very small sets of genes that lead to good classification results. Some of the selected genes are clearly correlated with known aspects of the biology of arthritis and, in some cases, reflect already known differences between RA and OA. Copyright © 2003 John Wiley & Sons, Ltd.
language: eng
source: Alma/SFX Local Collection
identifier: ISSN: 0749-503X
fulltext: fulltext
issn:
  • 0749-503X
  • 1531-6912
  • 1097-0061
  • 1097-0061
  • 1532-6268
url: Link


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descriptionThe use of large‐scale microarray expression profiling to identify predictors of disease class has become of major interest. Beyond their impact in the clinical setting (i.e. improving diagnosis and treatment), these markers are also likely to provide clues on the molecular mechanisms underlining the diseases. In this paper we describe a new method for the identification of multiple gene predictors of disease class. The method is applied to the classification of two forms of arthritis that have a similar clinical endpoint but different underlying molecular mechanisms: rheumatoid arthritis (RA) and osteoarthritis (OA). We aim at both the classification of samples and the location of genes characterizing the different classes. We achieve both goals simultaneously by combining a binary probit model for classification with Bayesian variable selection methods to identify important genes. We find very small sets of genes that lead to good classification results. Some of the selected genes are clearly correlated with known aspects of the biology of arthritis and, in some cases, reflect already known differences between RA and OA. Copyright © 2003 John Wiley & Sons, Ltd.
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subjectArticle Subject ; Bayesian variable selection ; Biological and medical sciences ; Biology (General) ; classification ; Fundamental and applied biological sciences. Psychology ; gene expression profiling ; General aspects ; Genetics ; Mathematics in biology. Statistical analysis. Models. Metrology. Data processing in biology (general aspects) ; Research Article ; Science
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abstractThe use of large‐scale microarray expression profiling to identify predictors of disease class has become of major interest. Beyond their impact in the clinical setting (i.e. improving diagnosis and treatment), these markers are also likely to provide clues on the molecular mechanisms underlining the diseases. In this paper we describe a new method for the identification of multiple gene predictors of disease class. The method is applied to the classification of two forms of arthritis that have a similar clinical endpoint but different underlying molecular mechanisms: rheumatoid arthritis (RA) and osteoarthritis (OA). We aim at both the classification of samples and the location of genes characterizing the different classes. We achieve both goals simultaneously by combining a binary probit model for classification with Bayesian variable selection methods to identify important genes. We find very small sets of genes that lead to good classification results. Some of the selected genes are clearly correlated with known aspects of the biology of arthritis and, in some cases, reflect already known differences between RA and OA. Copyright © 2003 John Wiley & Sons, Ltd.
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