These tools will no longer be maintained as of December 31, 2024. Archived website can be found here. PubMed4Hh GitHub repository can be found here. Contact NLM Customer Service if you have questions.


BIOMARKERS

Molecular Biopsy of Human Tumors

- a resource for Precision Medicine *

133 related articles for article (PubMed ID: 37440868)

  • 21. Bayesian biclustering for microbial metagenomic sequencing data via multinomial matrix factorization.
    Zhou F; He K; Li Q; Chapkin RS; Ni Y
    Biostatistics; 2022 Jul; 23(3):891-909. PubMed ID: 33634824
    [TBL] [Abstract][Full Text] [Related]  

  • 22. metamicrobiomeR: an R package for analysis of microbiome relative abundance data using zero-inflated beta GAMLSS and meta-analysis across studies using random effects models.
    Ho NT; Li F; Wang S; Kuhn L
    BMC Bioinformatics; 2019 Apr; 20(1):188. PubMed ID: 30991942
    [TBL] [Abstract][Full Text] [Related]  

  • 23. Transformation and differential abundance analysis of microbiome data incorporating phylogeny.
    Zhou C; Zhao H; Wang T
    Bioinformatics; 2021 Dec; 37(24):4652-4660. PubMed ID: 34302462
    [TBL] [Abstract][Full Text] [Related]  

  • 24. An integrative Bayesian Dirichlet-multinomial regression model for the analysis of taxonomic abundances in microbiome data.
    Wadsworth WD; Argiento R; Guindani M; Galloway-Pena J; Shelburne SA; Vannucci M
    BMC Bioinformatics; 2017 Feb; 18(1):94. PubMed ID: 28178947
    [TBL] [Abstract][Full Text] [Related]  

  • 25. A Bayesian Semiparametric Regression Model for Joint Analysis of Microbiome Data.
    Lee J; Sison-Mangus M
    Front Microbiol; 2018; 9():522. PubMed ID: 29632519
    [TBL] [Abstract][Full Text] [Related]  

  • 26. Estimating and comparing microbial diversity in the presence of sequencing errors.
    Chiu CH; Chao A
    PeerJ; 2016; 4():e1634. PubMed ID: 26855872
    [TBL] [Abstract][Full Text] [Related]  

  • 27. Impact of microbial count distributions on human health risk estimates.
    Duarte AS; Nauta MJ
    Int J Food Microbiol; 2015 Feb; 195():48-57. PubMed ID: 25506750
    [TBL] [Abstract][Full Text] [Related]  

  • 28. Zero-Inflated gaussian mixed models for analyzing longitudinal microbiome data.
    Zhang X; Guo B; Yi N
    PLoS One; 2020; 15(11):e0242073. PubMed ID: 33166356
    [TBL] [Abstract][Full Text] [Related]  

  • 29. Modelling of zero-inflation improves inference of metagenomic gene count data.
    Jonsson V; Österlund T; Nerman O; Kristiansson E
    Stat Methods Med Res; 2019 Dec; 28(12):3712-3728. PubMed ID: 30474490
    [TBL] [Abstract][Full Text] [Related]  

  • 30. Nonlinear mixed-effects modeling of longitudinal count data: Bayesian inference about median counts based on the marginal zero-inflated discrete Weibull distribution.
    Burger DA; Lesaffre E
    Stat Med; 2021 Oct; 40(23):5078-5095. PubMed ID: 34155664
    [TBL] [Abstract][Full Text] [Related]  

  • 31. An adaptive independence test for microbiome community data.
    Song Y; Zhao H; Wang T
    Biometrics; 2020 Jun; 76(2):414-426. PubMed ID: 31538660
    [TBL] [Abstract][Full Text] [Related]  

  • 32. A two-part mixed-effects model for analyzing longitudinal microbiome compositional data.
    Chen EZ; Li H
    Bioinformatics; 2016 Sep; 32(17):2611-7. PubMed ID: 27187200
    [TBL] [Abstract][Full Text] [Related]  

  • 33. A Bayesian framework for identifying consistent patterns of microbial abundance between body sites.
    Meier R; Thompson JA; Chung M; Zhao N; Kelsey KT; Michaud DS; Koestler DC
    Stat Appl Genet Mol Biol; 2019 Nov; 18(6):. PubMed ID: 31702998
    [TBL] [Abstract][Full Text] [Related]  

  • 34. Compositional zero-inflated network estimation for microbiome data.
    Ha MJ; Kim J; Galloway-Peña J; Do KA; Peterson CB
    BMC Bioinformatics; 2020 Dec; 21(Suppl 21):581. PubMed ID: 33371887
    [TBL] [Abstract][Full Text] [Related]  

  • 35. Confronting different models of community structure to species-abundance data: a Bayesian model comparison.
    Etienne RS; Olff H
    Ecol Lett; 2005 May; 8(5):493-504. PubMed ID: 21352453
    [TBL] [Abstract][Full Text] [Related]  

  • 36. Conditional Regression Based on a Multivariate Zero-Inflated Logistic-Normal Model for Microbiome Relative Abundance Data.
    Li Z; Lee K; Karagas MR; Madan JC; Hoen AG; O'Malley AJ; Li H
    Stat Biosci; 2018 Dec; 10(3):587-608. PubMed ID: 30923584
    [TBL] [Abstract][Full Text] [Related]  

  • 37. A comparison of statistical methods for modeling count data with an application to hospital length of stay.
    Fernandez GA; Vatcheva KP
    BMC Med Res Methodol; 2022 Aug; 22(1):211. PubMed ID: 35927612
    [TBL] [Abstract][Full Text] [Related]  

  • 38. Joint modeling of zero-inflated longitudinal proportions and time-to-event data with application to a gut microbiome study.
    Hu J; Wang C; Blaser MJ; Li H
    Biometrics; 2022 Dec; 78(4):1686-1698. PubMed ID: 34213763
    [TBL] [Abstract][Full Text] [Related]  

  • 39. Bivariate zero-inflated regression for count data: a Bayesian approach with application to plant counts.
    Majumdar A; Gries C
    Int J Biostat; 2010; 6(1):Article 27. PubMed ID: 21969981
    [TBL] [Abstract][Full Text] [Related]  

  • 40. A Dirichlet-Multinomial Bayes Classifier for Disease Diagnosis with Microbial Compositions.
    Gao X; Lin H; Dong Q
    mSphere; 2017; 2(6):. PubMed ID: 29242838
    [TBL] [Abstract][Full Text] [Related]  

    [Previous]   [Next]    [New Search]
    of 7.