Gene selection using a two-level hierarchical Bayesian model.
about
The horseshoe estimator for sparse signalsA hierarchical Naïve Bayes Model for handling sample heterogeneity in classification problems: an application to tissue microarrays.Biological assessment of robust noise models in microarray data analysis.A Bayesian model for joint analysis of multivariate repeated measures and time to event data in crossover trials.Bayesian variable selection and estimation in semiparametric joint models of multivariate longitudinal and survival data.Statistics and bioinformatics in nutritional sciences: analysis of complex data in the era of systems biology.Predictive response-relevant clustering of expression data provides insights into disease processes.Bayesian sparse graphical models and their mixtures.Bayesian analysis of genetic interactions in case-control studies, with application to adiponectin genes and colorectal cancer risk.Metagenes associated with survival in non-small cell lung cancer.Bayesian hierarchical structured variable selection methods with application to MIP studies in breast cancerSemi-Supervised Projective Non-Negative Matrix Factorization for Cancer ClassificationA Bayesian approach for inducing sparsity in generalized linear models with multi-category responseBayesian assignment of gene ontology terms to gene expression experimentsA Bayesian Group Sparse Multi-Task Regression Model for Imaging Genetics.Bayesian LASSO for quantitative trait loci mappingA Bayesian hierarchical model with novel prior specifications for estimating HIV testing ratesBayes and empirical Bayes methods for reduced rank regression models in matched case-control studiesHierarchical generalized linear models for multiple quantitative trait locus mapping.Statistical analysis of genetic interactions.Hierarchical Bayesian formulations for selecting variables in regression models.A New Bayesian Lasso.Gene selection using support vector machines with non-convex penalty.Definition of Valid Proteomic Biomarkers: A Bayesian SolutionA Bayesian Framework for Statistical Inference from Gene Expression DataAdaptively capturing the heterogeneity of expression for cancer biomarker identification
P2860
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P2860
Gene selection using a two-level hierarchical Bayesian model.
description
2004 nî lūn-bûn
@nan
2004年の論文
@ja
2004年学术文章
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2004年学术文章
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2004年学术文章
@zh-cn
2004年学术文章
@zh-hans
2004年学术文章
@zh-my
2004年学术文章
@zh-sg
2004年學術文章
@yue
2004年學術文章
@zh-hant
name
Gene selection using a two-level hierarchical Bayesian model.
@en
Gene selection using a two-level hierarchical Bayesian model.
@nl
type
label
Gene selection using a two-level hierarchical Bayesian model.
@en
Gene selection using a two-level hierarchical Bayesian model.
@nl
prefLabel
Gene selection using a two-level hierarchical Bayesian model.
@en
Gene selection using a two-level hierarchical Bayesian model.
@nl
P356
P1433
P1476
Gene selection using a two-level hierarchical Bayesian model.
@en
P2093
Bani K Mallick
Kyounghwa Bae
P304
P356
10.1093/BIOINFORMATICS/BTH419
P407
P577
2004-07-15T00:00:00Z