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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...
Autores principales: | , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Dove Medical Press
2008
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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. |
format | Online Article Text |
id | pubmed-3169938 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2008 |
publisher | Dove Medical Press |
record_format | MEDLINE/PubMed |
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 |
work_keys_str_mv | AT lineugene identificationofsignificantgenesingenomicsusingbayesianvariableselectionmethods AT huanglungcheng identificationofsignificantgenesingenomicsusingbayesianvariableselectionmethods |