Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

The Binomial Theorem01:30

The Binomial Theorem

331
The Binomial Theorem is a foundational principle in algebra used to expand expressions raised to a power. It provides a structured approach for expanding binomials of the form (a+b)n, where a and b are variables or constants representing algebraic expressions, and n is a non-negative integer.The general form of the Binomial Theorem is:Each term in the expansion involves a binomial coefficient, which is calculated using factorials:The exponent of a in each term decreases from n to 0, while the...
331
Binomial Probability Distribution01:15

Binomial Probability Distribution

15.9K
A binomial distribution is a probability distribution for a procedure with a fixed number of trials, where each trial can have only two outcomes.
The outcomes of a binomial experiment fit a binomial probability distribution. A statistical experiment can be classified as a binomial experiment if the following conditions are met:
There are a fixed number of trials. Think of trials as repetitions of an experiment. The letter n denotes the number of trials.
There are only two possible outcomes,...
15.9K
Analysis Methods of Pharmacokinetic Data: Model and Model-Independent Approaches01:14

Analysis Methods of Pharmacokinetic Data: Model and Model-Independent Approaches

541
Drug disposition in the body is a complex process and can be studied using two major approaches: the model and the model-independent approaches.
The model approach uses mathematical models to describe changes in drug concentration over time. Pharmacokinetic models help characterize drug behavior in patients, predict drug concentration in the body fluids, calculate optimum dosage regimens, and evaluate the risk of toxicity. However, ensuring that the model fits the experimental data accurately...
541
Model-Independent Approaches for Pharmacokinetic Data: Noncompartmental Analysis00:59

Model-Independent Approaches for Pharmacokinetic Data: Noncompartmental Analysis

335
Noncompartmental analyses offer an alternative method for describing drug pharmacokinetics without relying on a specific compartmental model. In this approach, the drug's pharmacokinetics are assumed to be linear, with the terminal phase log-linear. This assumption allows for simplified analysis and interpretation of the drug's behavior in the body.
One important characteristic of noncompartmental analyses is that drug exposure increases proportionally with increasing doses. This...
335
Binomial Expansion Using Pascal's Triangle01:30

Binomial Expansion Using Pascal's Triangle

264
Expanding a binomial expression such as (a + b)n results in a predictable sequence of terms that can be systematically derived using Pascal’s Triangle. This triangular array of numbers plays a central role in understanding and computing the coefficients of binomial expansions.Pascal’s Triangle is constructed such that each row corresponds to the coefficients of a binomial raised to a power. The topmost row, known as the zeroth row, corresponds to (a + b)0, and each successive row...
264
Analysis of Population Pharmacokinetic Data01:12

Analysis of Population Pharmacokinetic Data

758
Analysis of population pharmacokinetic data involves studying the behavior of drugs within diverse populations to understand their pharmacokinetic parameters. Traditional pharmacokinetic methods typically involve collecting samples from a few individuals and estimating these parameters. While these methods are commonly used, they have limitations in capturing the variability in drug response among individuals or heterogeneous populations. Population pharmacokinetics is employed to address these...
758

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Liver eQTL meta-analysis illuminates potential molecular mechanisms of cardiometabolic traits.

American journal of human genetics·2024
Same author

Exploring the Limits of Combined Image/'omics Analysis for Non-cancer Histological Phenotypes.

Frontiers in genetics·2020
Same author

A Review and Tutorial of Machine Learning Methods for Microbiome Host Trait Prediction.

Frontiers in genetics·2019
Same author

The enhanced angiogenic responses to ionic dissolution products from a boron-incorporated calcium silicate coating.

Materials science & engineering. C, Materials for biological applications·2019
Same author

βC1 protein encoded in geminivirus satellite concertedly targets MKK2 and MPK4 to counter host defense.

PLoS pathogens·2019
Same author

Evaluation of the effect of a toothpaste containing Pudilan extract on inhibiting plaques and reducing chronic gingivitis: A randomized, double-blinded, parallel controlled clinical trial.

Journal of ethnopharmacology·2019
Same journal

On the Connections Among Three Transfer Learning Paradigms.

Stat (International Statistical Institute)·2025
Same journal

Accelerating Resident Research within Quantitative Collaboration Units in Academic Healthcare.

Stat (International Statistical Institute)·2025
Same journal

Multivariate differential association analysis.

Stat (International Statistical Institute)·2024
Same journal

Developing partnerships for academic data science consulting and collaboration units.

Stat (International Statistical Institute)·2024
Same journal

Deep learning models to predict primary open-angle glaucoma.

Stat (International Statistical Institute)·2024
Same journal

What is it that you say you do here? Advocating for the critical role of data scientists in research infrastructure.

Stat (International Statistical Institute)·2024
See all related articles

Related Experiment Video

Updated: Feb 5, 2026

Application of Flow Vermimetry for Quantification and Analysis of the Caenorhabditis elegans Gut Microbiome
08:38

Application of Flow Vermimetry for Quantification and Analysis of the Caenorhabditis elegans Gut Microbiome

Published on: March 31, 2023

1.2K

A Zero-inflated Beta-binomial Model for Microbiome Data Analysis.

Tao Hu1, Paul Gallins1, Yi-Hui Zhou2

  • 1Bioinformatics Research Center, North Carolina State University, NC, 27695.

Stat (International Statistical Institute)
|September 11, 2018
PubMed
Summary
This summary is machine-generated.

This study introduces a new statistical method, zero-inflated beta-binomial (ZIBB), for analyzing microbiome data. ZIBB improves the detection of microbial taxa associated with host health phenotypes.

Keywords:
count datapenalized generalized linear modelzero inflated beta binomial modeling

More Related Videos

Oral Biofilm Sampling for Microbiome Analysis in Healthy Children
10:42

Oral Biofilm Sampling for Microbiome Analysis in Healthy Children

Published on: December 31, 2017

17.9K
Assisted Selection of Biomarkers by Linear Discriminant Analysis Effect Size LEfSe in Microbiome Data
04:57

Assisted Selection of Biomarkers by Linear Discriminant Analysis Effect Size LEfSe in Microbiome Data

Published on: May 16, 2022

17.5K

Related Experiment Videos

Last Updated: Feb 5, 2026

Application of Flow Vermimetry for Quantification and Analysis of the Caenorhabditis elegans Gut Microbiome
08:38

Application of Flow Vermimetry for Quantification and Analysis of the Caenorhabditis elegans Gut Microbiome

Published on: March 31, 2023

1.2K
Oral Biofilm Sampling for Microbiome Analysis in Healthy Children
10:42

Oral Biofilm Sampling for Microbiome Analysis in Healthy Children

Published on: December 31, 2017

17.9K
Assisted Selection of Biomarkers by Linear Discriminant Analysis Effect Size LEfSe in Microbiome Data
04:57

Assisted Selection of Biomarkers by Linear Discriminant Analysis Effect Size LEfSe in Microbiome Data

Published on: May 16, 2022

17.5K

Area of Science:

  • Microbiome research
  • Host-microbe interactions
  • Bioinformatics and computational biology

Background:

  • The host microbiome plays a crucial role in host health and can serve as a biomarker.
  • Metagenomic sequencing enables simultaneous analysis of thousands of microbial taxa.
  • Existing data analysis frameworks for microbiome data require improvement for accuracy and power.

Purpose of the Study:

  • To develop a novel statistical model for microbiome count data analysis.
  • To identify microbial taxa associated with host phenotypes.
  • To improve the power and accuracy of microbiome association studies.

Main Methods:

  • Introduction of a zero-inflated beta-binomial (ZIBB) model.
  • The ZIBB model is a mixture model with a zero component and a beta-binomial regression component.
  • The model accounts for excess zeros and overdispersion in microbiome count data.
  • Exploits mean-variance relationships and adjusts for covariates.

Main Results:

  • Simulation studies demonstrate effective control of Type I error rates.
  • The ZIBB method exhibits higher statistical power in detecting phenotype-associated taxa compared to competing methods.
  • The proposed method enhances the ability to find significant microbial biomarkers.

Conclusions:

  • The ZIBB model offers a robust and powerful approach for microbiome association studies.
  • This method improves the identification of microbial contributors to host phenotypes.
  • An R package, ZIBBSeqDiscovery, is available for practical application of the method.