ArticleBioinformatics (Oxford, England)2019
BhGLM: Bayesian hierarchical GLMs and survival models, with applications to genomics and epidemiology.
Article in Bioinformatics (Oxford, England), 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 28 papers.
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Who cites it
28 citing papers in PubMed.
- Urinary metabolomics may improve prediction of overall survival beyond tumor stage in colorectal cancer: results from the ColoCare study.Metabolomics : Official journal of the Metabolomic Society · 2026Article
- BCGLMs: Bayesian modeling for disease prediction using compositional microbiome features.Bioinformatics advances · 2026Article
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- Plasma pTau217 predicts continuous brain amyloid levels in preclinical and early Alzheimer's disease.Alzheimer's & dementia : the journal of the Alzheimer's Association · 2024Article
- A novel non-negative Bayesian stacking modeling method for Cancer survival prediction using high-dimensional omics data.BMC medical research methodology · 2024Article
- Bayesian Inference for High Dimensional Cox Models with Gaussian and Diffused-Gamma Priors: A Case Study of Mortality in COVID-19 Patients Admitted to the ICU.Statistics in biosciences · 2024Article
- A non-negative spike-and-slab lasso generalized linear stacking prediction modeling method for high-dimensional omics data.BMC bioinformatics · 2024Article
- Development and validation of a cuproptosis-related prognostic model for acute myeloid leukemia patients using machine learning with stacking.Scientific reports · 2024Article
- Predicting body fat percentage at 36 weeks of postmenstrual age in infants born preterm: A diagnostic accuracy study.JPEN. Journal of parenteral and enteral nutrition · 2023Article
- Prognostic risk of immune-associated signature in the microenvironment of brain gliomas.Frontiers in genetics · 2023Article
- Infants' gut microbiome data: A Bayesian Marginal Zero-inflated Negative Binomial regression model for multivariate analyses of count data.Computational and structural biotechnology journal · 2023Article
- Rationale and study protocol for a randomized controlled feeding study to determine the structural- and functional-level effects of diet-specific interventions on the gut microbiota of non-Hispanic black and white adults.Contemporary clinical trials · 2022Article
- Technical report: an online international weight control registry to inform precision approaches to healthy weight management.International journal of obesity (2005) · 2022Article
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- Diet Quality and the Gut Microbiota in Women Living in Alabama.American journal of preventive medicine · 2022Observational
- A novel 14-gene signature for overall survival in lung adenocarcinoma based on the Bayesian hierarchical Cox proportional hazards model.Scientific reports · 2022Article
- Bayesian hierarchical lasso Cox model: A 9-gene prognostic signature for overall survival in gastric cancer in an Asian population.PloS one · 2022Article
- Reboot: a straightforward approach to identify genes and splicing isoforms associated with cancer patient prognosis.NAR cancer · 2021Article
- Bayesian penalized cumulative logit model for high-dimensional data with an ordinal response.Statistics in medicine · 2021Article
Corrections and comments
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Authors and funding
4 authors.
Funding
Abstract
summaryBhGLM 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. AVAILABILITY AND IMPLEMENTATION: The package is freely available from the public GitHub repository, https://github.com/nyiuab/BhGLM.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.