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BhGLM specifications


Unique identifier OMICS_10166
Name BhGLM
Alternative name Bayesian hierarchical generalized linear models and survival models
Software type Package/Module
Interface Command line interface
Restrictions to use None
Operating system Unix/Linux, Mac OS, Windows
Programming languages R
License GNU General Public License version 3.0
Computer skills Advanced
Version 1.1.0
Stability Stable
Maintained Yes


  • Gsslasso Cox




No version available


  • person_outline Nengjun Yi
  • person_outline Xinyan Zhang

Additional information

Publications for Bayesian hierarchical generalized linear models and survival models

BhGLM citations


Predicting multi level drug response with gene expression profile in multiple myeloma using hierarchical ordinal regression

BMC Cancer
PMCID: 5946496
PMID: 29747599
DOI: 10.1186/s12885-018-4483-6

[…] rarchical ordinal model by finding the posterior mode of the parameters (β, c), i.e., estimating the parameters by maximizing the posterior density.Our algorithm has been implemented in our R package BhGLM, which is freely available from the website and the public GitHub repository that includes R codes for examples. […]


A Bayesian Semiparametric Regression Model for Joint Analysis of Microbiome Data

Front Microbiol
PMCID: 5879107
PMID: 29632519
DOI: 10.3389/fmicb.2018.00522
call_split See protocol

[…] g methods, the NBMM performs separate analyses of OTUs. An iterative weighted least squares algorithm is developed to produce the MLEs under the NBMM and implemented in a R function glmm in R package BhGLM. […]


Identification of lung adenocarcinoma specific dysregulated genes with diagnostic and prognostic value across 27 TCGA cancer types

PMCID: 5675633
PMID: 29152081
DOI: 10.18632/oncotarget.19823
call_split See protocol

[…] ith a log- rank test was used to assess patients’ survival using the R packages “survival”[]. The univariate and multivariable Cox proportional hazards regression were performed using the R packages “BhGLM”[]. Heatmaps were generated with z-score normalization with each column using R packages “gplots”[]. All analyses were performed using R software (version 3.2.2). A statistically significant dif […]


pLARmEB: integration of least angle regression with empirical Bayes for multilocus genome wide association studies

PMCID: 5436030
PMID: 28295030
DOI: 10.1038/hdy.2017.8

[…] the first experiment, each simulated sample was analyzed by pLARmEB, least angle regression plus empirical Bayes (LARmEB), EMMA, FASTmrEMMA, mrMLM and Bayesian hierarchical generalized linear model (BhGLM). Among the 1000 samples, the first 100 were further analyzed using the BhGLM method. As shown in and , the average power for the above 6 methods was 77.1, 68.9, 46.0, 70.7, 68.6 and 54.5%, res […]


Negative binomial mixed models for analyzing microbiome count data

BMC Bioinformatics
PMCID: 5209949
PMID: 28049409
DOI: 10.1186/s12859-016-1441-7

[…] The method has been incorporated into the freely available R package BhGLM ( and Mouse gut microbiome data set are obtained from Leamy et al. [] (Leamy LJ, Kelly SA, Nietfeldt J, Legge RM, Ma F, et al. (20 […]


Whole Genome Quantitative Trait Locus Mapping Reveals Major Role of Epistasis on Yield of Rice

PLoS One
PMCID: 3906158
PMID: 24489897
DOI: 10.1371/journal.pone.0087330

[…] ent. Our previous studies demonstrated that EBlasso outperformed several other multiple QTL mapping methods including the empirical Bayes method , the Bayesian hierarchical generalized linear models (BhGLM) , HyperLasso , and Lasso . Detailed description of the EBlasso algorithm can be found in , and an efficient C program with the R interface implementing the EBlasso algorithm is available.The […]


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BhGLM institution(s)
Department of Biostatistics, School of Public Health, University of Alabama at Birmingham, Birmingham, AL, USA; Department of Biostatistics, School of Public Health, Medical College of Soochow University, Suzhou, China; Department of Biostatistics, Jiann-Ping Hsu College of Public Health, Georgia Southern University, Statesboro, GA, USA
BhGLM funding source(s)
Supported by NIH R01GM069430, R01CA216108, and the National Natural Science Foundation of China (81773541, 81573253).

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