Bayesian compositional regression with microbiome features via variational inference

[1]  Dhwani K. Desai,et al.  Microbiome differential abundance methods produce different results across 38 datasets , 2022, Nature Communications.

[2]  Dhwani K. Desai,et al.  Microbiome differential abundance methods produce disturbingly different results across 38 datasets , 2021, bioRxiv.

[3]  K. McCormick,et al.  The role of the gut microbiota on the metabolic status of obese children , 2021, Microbial Cell Factories.

[4]  R. Guerra-Sá,et al.  High‐sugar diet intake, physical activity, and gut microbiota crosstalk: Implications for obesity in rats , 2020, Food science & nutrition.

[5]  Robert R Jenq,et al.  Bayesian compositional regression with structured priors for microbiome feature selection , 2020, Biometrics.

[6]  Zhicong Yang,et al.  High Oscillospira abundance indicates constipation and low BMI in the Guangdong Gut Microbiome Project , 2020, Scientific Reports.

[7]  M. Vannucci,et al.  A Bayesian model of microbiome data for simultaneous identification of covariate associations and prediction of phenotypic outcomes , 2020, The Annals of Applied Statistics.

[8]  Maria De Iorio,et al.  Monte Carlo co-ordinate ascent variational inference , 2019, Stat. Comput..

[9]  Christian L. Müller,et al.  Regression Models for Compositional Data: General Log-Contrast Formulations, Proximal Optimization, and Microbiome Data Applications , 2019, Statistics in Biosciences.

[10]  Chun-Ying Wu,et al.  The gut microbiome in obesity. , 2019, Journal of the Formosan Medical Association = Taiwan yi zhi.

[11]  Shuang Jiang,et al.  Bayesian Modeling of Microbiome Data for Differential Abundance Analysis. , 2019, 1902.08741.

[12]  T. Clark,et al.  Know Your Heart: Rationale, design and conduct of a cross-sectional study of cardiovascular structure, function and risk factors in 4500 men and women aged 35-69 years from two Russian cities, 2015-18 , 2018, Wellcome open research.

[13]  Benjamin D. Kaehler,et al.  Optimizing taxonomic classification of marker-gene amplicon sequences with QIIME 2’s q2-feature-classifier plugin , 2018, Microbiome.

[14]  Benjamin D. Kaehler,et al.  Optimizing taxonomic classification of marker-gene amplicon sequences with QIIME 2’s q2-feature-classifier plugin , 2018, Microbiome.

[15]  R. Paredes,et al.  Balances: a New Perspective for Microbiome Analysis , 2017, mSystems.

[16]  Jean M. Macklaim,et al.  Microbiome Datasets Are Compositional: And This Is Not Optional , 2017, Front. Microbiol..

[17]  Robert Tibshirani,et al.  Log‐ratio lasso: Scalable, sparse estimation for log‐ratio models , 2017, Biometrics.

[18]  Anthony C Davison,et al.  Efficient inference for genetic association studies with multiple outcomes , 2016, Biostatistics.

[19]  C. Davis.  The Gut Microbiome and Its Role in Obesity. , 2016, Nutrition today.

[20]  Anru R. Zhang,et al.  Regression Analysis for Microbiome Compositional Data , 2016, 1603.00974.

[21]  F. Groppo,et al.  Correlation between body mass index and faecal microbiota from children. , 2016, Clinical microbiology and infection : the official publication of the European Society of Clinical Microbiology and Infectious Diseases.

[22]  R. Milo,et al.  Revised Estimates for the Number of Human and Bacteria Cells in the Body , 2016, bioRxiv.

[23]  David M. Blei,et al.  Variational Inference: A Review for Statisticians , 2016, ArXiv.

[24]  Xiaofang Xu,et al.  Bayesian Variable Selection and Estimation for Group Lasso , 2015, 1512.01013.

[25]  Aida Moreno-Moral,et al.  MT-HESS: an efficient Bayesian approach for simultaneous association detection in OMICS datasets, with application to eQTL mapping in multiple tissues , 2015, Bioinform..

[26]  C. Huttenhower,et al.  Sequencing and beyond: integrating molecular 'omics' for microbial community profiling , 2015, Nature Reviews Microbiology.

[27]  Hongzhe Li.  Microbiome, Metagenomics, and High-Dimensional Compositional Data Analysis , 2015 .

[28]  Hongzhe Li,et al.  Variable selection in regression with compositional covariates , 2014 .

[29]  Max Welling,et al.  UvA-DARE (Digital Academic Repository) Bayesian structure learning for Markov Random Fields with a spike and slab , 2012 .

[30]  David M. Blei,et al.  Structured Stochastic Variational Inference , 2014, 1404.4114.

[31]  E. Zoetendal,et al.  Human intestinal microbiota composition is associated with local and systemic inflammation in obesity , 2013, Obesity.

[32]  Jim E. Griffin,et al.  Adaptive Monte Carlo for Bayesian Variable Selection in Regression Models , 2013 .

[33]  Tim Salimans,et al.  Fixed-Form Variational Posterior Approximation through Stochastic Linear Regression , 2012, ArXiv.

[34]  P. Filzmoser,et al.  Linear regression with compositional explanatory variables , 2012 .

[35]  M. Stephens,et al.  Scalable Variational Inference for Bayesian Variable Selection in Regression, and Its Accuracy in Genetic Association Studies , 2012 .

[36]  Sujit K. Ghosh,et al.  A Bayesian Approach to Multicollinearity and the Simultaneous Selection and Clustering of Predictors in Linear Regression , 2011 .

[37]  M. Stephens,et al.  Bayesian variable selection regression for genome-wide association studies and other large-scale problems , 2011, 1110.6019.

[38]  James G. Scott,et al.  Bayes and empirical-Bayes multiplicity adjustment in the variable-selection problem , 2010, 1011.2333.

[39]  Chenlei Leng,et al.  Bayesian adaptive Lasso , 2010, Annals of the Institute of Statistical Mathematics.

[40]  M. Wand,et al.  Explaining Variational Approximations , 2010 .

[41]  A. Schwiertz,et al.  Microbiota and SCFA in Lean and Overweight Healthy Subjects , 2010, Obesity.

[42]  Fabrice Armougom,et al.  Monitoring Bacterial Community of Human Gut Microbiota Reveals an Increase in Lactobacillus in Obese Patients and Methanogens in Anorexic Patients , 2009, PloS one.

[43]  Jim E. Griffin,et al.  Transdimensional Sampling Algorithms for Bayesian Variable Selection in Classification Problems With Many More Variables Than Observations , 2009 .

[44]  G. Casella,et al.  The Bayesian Lasso , 2008 .

[45]  David J. Nott,et al.  Adaptive sampling for Bayesian variable selection , 2005 .

[46]  Charles M. Bishop,et al.  Variational Message Passing , 2005, J. Mach. Learn. Res..

[47]  Antti Honkela,et al.  Unsupervised Variational Bayesian Learning of Nonlinear Models , 2004, NIPS.

[48]  G. Mateu-Figueras,et al.  Isometric Logratio Transformations for Compositional Data Analysis , 2003 .

[49]  Christopher M. Bishop,et al.  Bayesian Hierarchical Mixtures of Experts , 2002, UAI.

[50]  Michael I. Jordan,et al.  An Introduction to Variational Methods for Graphical Models , 1999, Machine Learning.

[51]  E. George,et al.  APPROACHES FOR BAYESIAN VARIABLE SELECTION , 1997 .

[52]  P. Green.  Reversible jump Markov chain Monte Carlo computation and Bayesian model determination , 1995 .

[53]  John Aitchison,et al.  The Statistical Analysis of Compositional Data , 1986 .

[54]  J. Aitchison,et al.  Log contrast models for experiments with mixtures , 1984 .

[55]  B. Mallick.  VARIABLE SELECTION FOR REGRESSION MODELS , 2016 .

[56]  Christopher M. Bishop,et al.  UAI'03 Proceedings of the Nineteenth conference on Uncertainty in Artificial Intelligence , 2003 .

[57]  Petros Dellaportas,et al.  On Bayesian model and variable selection using MCMC , 2002, Stat. Comput..

[58]  Michael I. Jordan,et al.  A Variational Approach to Bayesian Logistic Regression Models and their Extensions , 1997, AISTATS.

[59]  R. Tibshirani.  Regression Shrinkage and Selection via the Lasso , 1996 .

[60]  J. Atchison,et al.  Logistic-normal distributions:Some properties and uses , 1980 .

[61]  Ove Frank,et al.  Journal of the American Statistical Association is currently published by American Statistical Association. , 2007 .