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Applications of Bayesian Gene Selection and Classification with Mixtures of Generalized Singular g-Priors

Recent advancement in microarray technologies has led to a collection of an enormous number of genetic markers in disease association studies, and yet scientists are interested in selecting a smaller set of genes to explore the relation between genes and disease. Current approaches either adopt a si...

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Detalles Bibliográficos
Autores principales: Chien, Wen-Kuei, Hsiao, Chuhsing Kate
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Hindawi Publishing Corporation 2013
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3870637/
https://www.ncbi.nlm.nih.gov/pubmed/24382981
http://dx.doi.org/10.1155/2013/420412
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author Chien, Wen-Kuei
Hsiao, Chuhsing Kate
author_facet Chien, Wen-Kuei
Hsiao, Chuhsing Kate
author_sort Chien, Wen-Kuei
collection PubMed
description Recent advancement in microarray technologies has led to a collection of an enormous number of genetic markers in disease association studies, and yet scientists are interested in selecting a smaller set of genes to explore the relation between genes and disease. Current approaches either adopt a single marker test which ignores the possible interaction among genes or consider a multistage procedure that reduces the large size of genes before evaluation of the association. Among the latter, Bayesian analysis can further accommodate the correlation between genes through the specification of a multivariate prior distribution and estimate the probabilities of association through latent variables. The covariance matrix, however, depends on an unknown parameter. In this research, we suggested a reference hyperprior distribution for such uncertainty, outlined the implementation of its computation, and illustrated this fully Bayesian approach with a colon and leukemia cancer study. Comparison with other existing methods was also conducted. The classification accuracy of our proposed model is higher with a smaller set of selected genes. The results not only replicated findings in several earlier studies, but also provided the strength of association with posterior probabilities.
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spelling pubmed-38706372014-01-01 Applications of Bayesian Gene Selection and Classification with Mixtures of Generalized Singular g-Priors Chien, Wen-Kuei Hsiao, Chuhsing Kate Comput Math Methods Med Research Article Recent advancement in microarray technologies has led to a collection of an enormous number of genetic markers in disease association studies, and yet scientists are interested in selecting a smaller set of genes to explore the relation between genes and disease. Current approaches either adopt a single marker test which ignores the possible interaction among genes or consider a multistage procedure that reduces the large size of genes before evaluation of the association. Among the latter, Bayesian analysis can further accommodate the correlation between genes through the specification of a multivariate prior distribution and estimate the probabilities of association through latent variables. The covariance matrix, however, depends on an unknown parameter. In this research, we suggested a reference hyperprior distribution for such uncertainty, outlined the implementation of its computation, and illustrated this fully Bayesian approach with a colon and leukemia cancer study. Comparison with other existing methods was also conducted. The classification accuracy of our proposed model is higher with a smaller set of selected genes. The results not only replicated findings in several earlier studies, but also provided the strength of association with posterior probabilities. Hindawi Publishing Corporation 2013 2013-12-08 /pmc/articles/PMC3870637/ /pubmed/24382981 http://dx.doi.org/10.1155/2013/420412 Text en Copyright © 2013 W.-K. Chien and C. K. Hsiao. https://creativecommons.org/licenses/by/3.0/ This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Research Article
Chien, Wen-Kuei
Hsiao, Chuhsing Kate
Applications of Bayesian Gene Selection and Classification with Mixtures of Generalized Singular g-Priors
title Applications of Bayesian Gene Selection and Classification with Mixtures of Generalized Singular g-Priors
title_full Applications of Bayesian Gene Selection and Classification with Mixtures of Generalized Singular g-Priors
title_fullStr Applications of Bayesian Gene Selection and Classification with Mixtures of Generalized Singular g-Priors
title_full_unstemmed Applications of Bayesian Gene Selection and Classification with Mixtures of Generalized Singular g-Priors
title_short Applications of Bayesian Gene Selection and Classification with Mixtures of Generalized Singular g-Priors
title_sort applications of bayesian gene selection and classification with mixtures of generalized singular g-priors
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3870637/
https://www.ncbi.nlm.nih.gov/pubmed/24382981
http://dx.doi.org/10.1155/2013/420412
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