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Identification of significant genes in genomics using Bayesian variable selection methods

In the studies of genomics, it is essential to select a small number of genes that are more significant than the others for research ranging from candidate gene studies to genome-wide association studies. In this study, we proposed a Bayesian method for identifying the promising candidate genes that...

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Detalles Bibliográficos
Autores principales: Lin, Eugene, Huang, Lung-Cheng
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Dove Medical Press 2008
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3169938/
https://www.ncbi.nlm.nih.gov/pubmed/21918603
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author Lin, Eugene
Huang, Lung-Cheng
author_facet Lin, Eugene
Huang, Lung-Cheng
author_sort Lin, Eugene
collection PubMed
description In the studies of genomics, it is essential to select a small number of genes that are more significant than the others for research ranging from candidate gene studies to genome-wide association studies. In this study, we proposed a Bayesian method for identifying the promising candidate genes that are significantly more influential than the others. We employed the framework of variable selection and a Gibbs sampling based technique to identify significant genes. The proposed approach was applied to a genomics study for persons with chronic fatigue syndrome. Our studies show that the proposed Bayesian methodology is effective for deriving models for genomic studies and for providing information on significant genes.
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spelling pubmed-31699382011-09-14 Identification of significant genes in genomics using Bayesian variable selection methods Lin, Eugene Huang, Lung-Cheng Adv Appl Bioinforma Chem Original Research In the studies of genomics, it is essential to select a small number of genes that are more significant than the others for research ranging from candidate gene studies to genome-wide association studies. In this study, we proposed a Bayesian method for identifying the promising candidate genes that are significantly more influential than the others. We employed the framework of variable selection and a Gibbs sampling based technique to identify significant genes. The proposed approach was applied to a genomics study for persons with chronic fatigue syndrome. Our studies show that the proposed Bayesian methodology is effective for deriving models for genomic studies and for providing information on significant genes. Dove Medical Press 2008-07-01 /pmc/articles/PMC3169938/ /pubmed/21918603 Text en © 2008 Lin and Huang, publisher and licensee Dove Medical Press Ltd. This is an Open Access article which permits unrestricted noncommercial use, provided the original work is properly cited.
spellingShingle Original Research
Lin, Eugene
Huang, Lung-Cheng
Identification of significant genes in genomics using Bayesian variable selection methods
title Identification of significant genes in genomics using Bayesian variable selection methods
title_full Identification of significant genes in genomics using Bayesian variable selection methods
title_fullStr Identification of significant genes in genomics using Bayesian variable selection methods
title_full_unstemmed Identification of significant genes in genomics using Bayesian variable selection methods
title_short Identification of significant genes in genomics using Bayesian variable selection methods
title_sort identification of significant genes in genomics using bayesian variable selection methods
topic Original Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3169938/
https://www.ncbi.nlm.nih.gov/pubmed/21918603
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