An integrative framework for Bayesian variable selection with informative priors for identifying genes and pathways.

The discovery of genetic or genomic markers plays a central role in the development of personalized medicine. A notable challenge exists when dealing with the high dimensionality of the data sets, as thousands of genes or millions of genetic variants are collected on a relatively small number of sub...

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Autores principales: Bin Peng, Dianwen Zhu, Bradley P Ander, Xiaoshuai Zhang, Fuzhong Xue, Frank R Sharp, Xiaowei Yang
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Lenguaje:EN
Publicado: Public Library of Science (PLoS) 2013
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Acceso en línea:https://doaj.org/article/7a6bb25d881f4b0abb71673cb8f7b99c
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spelling oai:doaj.org-article:7a6bb25d881f4b0abb71673cb8f7b99c2021-11-18T07:38:49ZAn integrative framework for Bayesian variable selection with informative priors for identifying genes and pathways.1932-620310.1371/journal.pone.0067672https://doaj.org/article/7a6bb25d881f4b0abb71673cb8f7b99c2013-01-01T00:00:00Zhttps://www.ncbi.nlm.nih.gov/pmc/articles/pmid/23844055/?tool=EBIhttps://doaj.org/toc/1932-6203The discovery of genetic or genomic markers plays a central role in the development of personalized medicine. A notable challenge exists when dealing with the high dimensionality of the data sets, as thousands of genes or millions of genetic variants are collected on a relatively small number of subjects. Traditional gene-wise selection methods using univariate analyses face difficulty to incorporate correlational, structural, or functional structures amongst the molecular measures. For microarray gene expression data, we first summarize solutions in dealing with 'large p, small n' problems, and then propose an integrative Bayesian variable selection (iBVS) framework for simultaneously identifying causal or marker genes and regulatory pathways. A novel partial least squares (PLS) g-prior for iBVS is developed to allow the incorporation of prior knowledge on gene-gene interactions or functional relationships. From the point view of systems biology, iBVS enables user to directly target the joint effects of multiple genes and pathways in a hierarchical modeling diagram to predict disease status or phenotype. The estimated posterior selection probabilities offer probabilitic and biological interpretations. Both simulated data and a set of microarray data in predicting stroke status are used in validating the performance of iBVS in a Probit model with binary outcomes. iBVS offers a general framework for effective discovery of various molecular biomarkers by combining data-based statistics and knowledge-based priors. Guidelines on making posterior inferences, determining Bayesian significance levels, and improving computational efficiencies are also discussed.Bin PengDianwen ZhuBradley P AnderXiaoshuai ZhangFuzhong XueFrank R SharpXiaowei YangPublic Library of Science (PLoS)articleMedicineRScienceQENPLoS ONE, Vol 8, Iss 7, p e67672 (2013)
institution DOAJ
collection DOAJ
language EN
topic Medicine
R
Science
Q
spellingShingle Medicine
R
Science
Q
Bin Peng
Dianwen Zhu
Bradley P Ander
Xiaoshuai Zhang
Fuzhong Xue
Frank R Sharp
Xiaowei Yang
An integrative framework for Bayesian variable selection with informative priors for identifying genes and pathways.
description The discovery of genetic or genomic markers plays a central role in the development of personalized medicine. A notable challenge exists when dealing with the high dimensionality of the data sets, as thousands of genes or millions of genetic variants are collected on a relatively small number of subjects. Traditional gene-wise selection methods using univariate analyses face difficulty to incorporate correlational, structural, or functional structures amongst the molecular measures. For microarray gene expression data, we first summarize solutions in dealing with 'large p, small n' problems, and then propose an integrative Bayesian variable selection (iBVS) framework for simultaneously identifying causal or marker genes and regulatory pathways. A novel partial least squares (PLS) g-prior for iBVS is developed to allow the incorporation of prior knowledge on gene-gene interactions or functional relationships. From the point view of systems biology, iBVS enables user to directly target the joint effects of multiple genes and pathways in a hierarchical modeling diagram to predict disease status or phenotype. The estimated posterior selection probabilities offer probabilitic and biological interpretations. Both simulated data and a set of microarray data in predicting stroke status are used in validating the performance of iBVS in a Probit model with binary outcomes. iBVS offers a general framework for effective discovery of various molecular biomarkers by combining data-based statistics and knowledge-based priors. Guidelines on making posterior inferences, determining Bayesian significance levels, and improving computational efficiencies are also discussed.
format article
author Bin Peng
Dianwen Zhu
Bradley P Ander
Xiaoshuai Zhang
Fuzhong Xue
Frank R Sharp
Xiaowei Yang
author_facet Bin Peng
Dianwen Zhu
Bradley P Ander
Xiaoshuai Zhang
Fuzhong Xue
Frank R Sharp
Xiaowei Yang
author_sort Bin Peng
title An integrative framework for Bayesian variable selection with informative priors for identifying genes and pathways.
title_short An integrative framework for Bayesian variable selection with informative priors for identifying genes and pathways.
title_full An integrative framework for Bayesian variable selection with informative priors for identifying genes and pathways.
title_fullStr An integrative framework for Bayesian variable selection with informative priors for identifying genes and pathways.
title_full_unstemmed An integrative framework for Bayesian variable selection with informative priors for identifying genes and pathways.
title_sort integrative framework for bayesian variable selection with informative priors for identifying genes and pathways.
publisher Public Library of Science (PLoS)
publishDate 2013
url https://doaj.org/article/7a6bb25d881f4b0abb71673cb8f7b99c
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