BhGLM: Bayesian hierarchical GLMs and survival models, with applications to genomics and epidemiology

Nengjun YiBoyi Guo


BhGLM is a freely available R package that implements Bayesian hierarchical modeling for high-dimensional clinical and genomic data. It consists of functions for setting up various Bayesian hierarchical models, including generalized linear models (GLMs) and Cox survival models, with four types of prior distributions for coefficients, i.e. double-exponential, Student-t, mixture double-exponential and mixture Student-t. These functions adapt fast and stable algorithms to estimate parameters. BhGLM also provides functions for summarizing results numerically and graphically and for evaluating predictive values. The package is particularly useful for analyzing large-scale molecular data, i.e. detecting disease-associated variables and predicting disease outcomes. We here describe the models, algorithms and associated features implemented in BhGLM. The package is freely available from the public GitHub repository,


Nov 30, 2012·Statistical Applications in Genetics and Molecular Biology·Nengjun Yi, Shuangge Ma
Jan 31, 2015·The New England Journal of Medicine·Francis S Collins, Harold Varmus
Mar 1, 2011·Journal of Statistical Software·Noah SimonRob Tibshirani
Jun 29, 2018·Bioinformatics·Wensheng ZhangKun Zhang

Related Concepts

Adapt (substance)
Human Activity Profile Test

Trending Feeds


Coronaviruses encompass a large family of viruses that cause the common cold as well as more serious diseases, such as the ongoing outbreak of coronavirus disease 2019 (COVID-19; formally known as 2019-nCoV). Coronaviruses can spread from animals to humans; symptoms include fever, cough, shortness of breath, and breathing difficulties; in more severe cases, infection can lead to death. This feed covers recent research on COVID-19.

Chronic Fatigue Syndrome

Chronic fatigue syndrome is a disease characterized by unexplained disabling fatigue; the pathology of which is incompletely understood. Discover the latest research on chronic fatigue syndrome here.

Synapse Loss as Therapeutic Target in MS

As we age, the number of synapses present in the human brain starts to decline, but in neurodegenerative diseases this occurs at an accelerated rate. In MS, it has been shown that there is a reduction in synaptic density, which presents a potential target for treatment. Here is the latest research on synapse loss as a therapeutic target in MS.

Artificial Intelligence in Cardiac Imaging

Artificial intelligence (ai) techniques are increasingly applied to cardiovascular (cv) medicine in cardiac imaging analysis. Here is the latest research.

Position Effect Variegation

Position Effect Variagation occurs when a gene is inactivated due to its positioning near heterochromatic regions within a chromosome. Discover the latest research on Position Effect Variagation here.

Social Learning

Social learning involves learning new behaviors through observation, imitation and modeling. Follow this feed to stay up to date on the latest research.

Cell Atlas of the Human Eye

Constructing a cell atlas of the human eye will require transcriptomic and histologic analysis over the lifespan. This understanding will aid in the study of development and disease. Find the latest research pertaining to the Cell Atlas of the Human Eye here.

Single Cell Chromatin Profiling

Techniques like ATAC-seq and CUT&Tag have the potential to allow single cell profiling of chromatin accessibility, histones, and TFs. This will provide novel insight into cellular heterogeneity and cell states. Discover the latest research on single cell chromatin profiling here.

Genetic Screens in iPSC-derived Brain Cells

Genetic screening is a critical tool that can be employed to define and understand gene function and interaction. This feed focuses on genetic screens conducted using induced pluripotent stem cell (iPSC)-derived brain cells.