ArticleBiometrics2021
Bayesian compositional regression with structured priors for microbiome feature selection.
Article in Biometrics, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
13 citing papers in PubMed.
- A joint mixture Tobit method with latent microbial abundance improves detection of microbiome-disease associations in zero-inflated data.PLoS computational biology · 2026Article
- Bayesian Sparse Regression for Microbiome-Metabolite Data Integration.Statistics in medicine · 2026Article
- A nutritionally informed model for Bayesian variable selection with metabolite response variables.Biostatistics (Oxford, England) · 2026Article
- BCGLMs: Bayesian modeling for disease prediction using compositional microbiome features.Bioinformatics advances · 2026Article
- Beyond Euclid: an illustrated guide to modern machine learning with geometric, topological, and algebraic structures.Machine learning: science and technology · 2025Review
- Bayesian compositional generalized linear mixed models for disease prediction using microbiome data.BMC bioinformatics · 2025Article
- Review and revamp of compositional data transformation: A new framework combining proportion conversion and contrast transformation.Computational and structural biotechnology journal · 2024Review
- Constructing phylogenetic trees for microbiome data analysis: A mini-review.Computational and structural biotechnology journal · 2024Review
- Analysis of Microbiome Data.Annual review of statistics and its application · 2024Article
- Bayesian compositional regression with microbiome features via variational inference.BMC bioinformatics · 2023Article
- Sparse tree-based clustering of microbiome data to characterize microbiome heterogeneity in pancreatic cancer.Journal of the Royal Statistical Society. Series C, Applied statistics · 2023Article
- Three approaches to supervised learning for compositional data with pairwise logratios.Journal of applied statistics · 2023Article
- Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
Abstract
The microbiome plays a critical role in human health and disease, and there is a strong scientific interest in linking specific features of the microbiome to clinical outcomes. There are key aspects of microbiome data, however, that limit the applicability of standard variable selection methods. In particular, the observed data are compositional, as the counts within each sample have a fixed-sum constraint. In addition, microbiome features, typically quantified as operational taxonomic units, often reflect microorganisms that are similar in function, and may therefore have a similar influence on the response variable. To address the challenges posed by these aspects of the data structure, we propose a variable selection technique with the following novel features: a generalized transformation and z-prior to handle the compositional constraint, and an Ising prior that encourages the joint selection of microbiome features that are closely related in terms of their genetic sequence similarity. We demonstrate that our proposed method outperforms existing penalized approaches for microbiome variable selection in both simulation and the analysis of real data exploring the relationship of the gut microbiome to body mass index.
Indexed as
Identifiers
What OpenQuestion holds
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.