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

Methods to Assess Microbial Communities01:19

Methods to Assess Microbial Communities

50
Microbial communities, comprising bacteria, archaea, and eukaryotic microorganisms, inhabit diverse ecosystems and play crucial roles in environmental and biological processes. Their diversity is defined by three main parameters: species richness (the number of distinct species), species abundance (the relative quantity of each species), and species evenness (how uniformly individual species are distributed in various locations). These factors together shape the structure and ecological balance...
50
Methods to Assess Microbial Populations01:30

Methods to Assess Microbial Populations

76
Assessing microbial populations is crucial for understanding microbial roles in health, ecology, and industry. Various complementary techniques—both culture-based and molecular—enable detailed analysis of microbial abundance, diversity, and function.Viable Plate CountThe viable plate count is a traditional culture-based method used to estimate the number of living microbes in a sample. After serial dilution, the sample is spread onto nutrient agar plates. Each viable cell forms a...
76
Phylogenetic Species Concept in Microbiology01:22

Phylogenetic Species Concept in Microbiology

147
The phylogenetic species concept (PSC) is a framework used to delineate species based on evolutionary relationships, emphasizing shared ancestry and diagnosable genetic traits. Unlike morphological or biological species concepts, the PSC is particularly advantageous for microbial taxonomy, where traditional reproductive or phenotypic criteria often fall short due to the prevalence of asexual reproduction, minimal morphological differentiation, and widespread horizontal gene transfer among...
147
Microbial Phylogeny01:28

Microbial Phylogeny

76
Understanding the evolutionary relationships among microorganisms is fundamental to microbial ecology and taxonomy. Phylogenetic trees are essential tools for inferring these relationships, relying primarily on comparative analyses of molecular sequences such as DNA, RNA, or proteins. In microbial studies, these trees typically depict the evolutionary paths of diverse bacterial and archaeal species by mapping genetic differences accumulated over time.Phylogenetic trees are composed of tips,...
76
Microbial Classification System01:24

Microbial Classification System

1.7K
Classification is the process of organizing organisms into hierarchically inclusive groups based on their phenotypic similarities or evolutionary relationships. A species comprises one or more strains, and closely related species are grouped into genera. Genera are further classified into families, families into orders, orders into classes, and so forth, up to the domain level, which is the broadest taxonomic rank derived from a combination of phenotypic and genotypic data.The nomenclature of...
1.7K
Model Approaches for Pharmacokinetic Data: Distributed Parameter Models01:06

Model Approaches for Pharmacokinetic Data: Distributed Parameter Models

319
Pharmacokinetic models are mathematical constructs that represent and predict the time course of drug concentrations in the body, providing meaningful pharmacokinetic parameters. These models are categorized into compartment, physiological, and distributed parameter models.
The distributed parameter models are specifically designed to account for variations and differences in some drug classes. This model is particularly useful for assessing regional concentrations of anticancer or...
319

You might also read

Related Articles

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

Sort by
Same author

"Shining a light on chronic pain": A qualitative study of stakeholder views towards chronic pain at work and the Pain-at-Work Toolkit.

PloS one·2026
Same author

The importance of central sensitization for clinical trials of disease modifying osteoarthritis drugs (DMOADs).

Osteoarthritis imaging·2026
Same author

Assessing central nervous system contributions to accelerate musculoskeletal pain diagnosis and treatment (AsCent): protocol for a mixed-method, prospective observational study.

BMJ open·2026
Same author

Prevalence of foot/ankle osteoarthritis and pain in retired male professional footballers compared with general population male controls: a cross-sectional study.

Rheumatology (Oxford, England)·2026
Same author

Central pain sensitivity is associated with changes in fatigue in RA: data from the CAP-RA study.

Rheumatology (Oxford, England)·2026
Same author

Rheumatoid arthritis and interstitial lung disease: the role of comorbidities-a retrospective analysis of two RA inception cohorts in the UK.

Rheumatology (Oxford, England)·2026

Related Experiment Video

Updated: Apr 16, 2026

Empirical, Metagenomic, and Computational Techniques Illuminate the Mechanisms by which Fungicides Compromise Bee Health
08:36

Empirical, Metagenomic, and Computational Techniques Illuminate the Mechanisms by which Fungicides Compromise Bee Health

Published on: October 9, 2017

10.5K

BioMiCo: a supervised Bayesian model for inference of microbial community structure.

Mahdi Shafiei1, Katherine A Dunn2, Eva Boon2

  • 1Department of Mathematics and Statistics, Dalhousie University, Halifax, NS Canada.

Microbiome
|March 17, 2015
PubMed
Summary

BioMiCo, a novel Bayesian model, analyzes complex microbial community data. It accurately predicts sample features like body site and health status by learning species assemblage patterns from sparse abundance data.

Keywords:
Admixture modelBayesian modelHierarchical mixed-membership modelHumanMicrobial community structureMicrobiomeOTU abundance dataSupervised learningTemperate coastal ocean

More Related Videos

Characterizing Microbiome Dynamics – Flow Cytometry Based Workflows from Pure Cultures to Natural Communities
09:57

Characterizing Microbiome Dynamics – Flow Cytometry Based Workflows from Pure Cultures to Natural Communities

Published on: July 12, 2018

12.6K
Efficient Nucleic Acid Extraction and 16S rRNA Gene Sequencing for Bacterial Community Characterization
12:37

Efficient Nucleic Acid Extraction and 16S rRNA Gene Sequencing for Bacterial Community Characterization

Published on: April 14, 2016

40.7K

Related Experiment Videos

Last Updated: Apr 16, 2026

Empirical, Metagenomic, and Computational Techniques Illuminate the Mechanisms by which Fungicides Compromise Bee Health
08:36

Empirical, Metagenomic, and Computational Techniques Illuminate the Mechanisms by which Fungicides Compromise Bee Health

Published on: October 9, 2017

10.5K
Characterizing Microbiome Dynamics – Flow Cytometry Based Workflows from Pure Cultures to Natural Communities
09:57

Characterizing Microbiome Dynamics – Flow Cytometry Based Workflows from Pure Cultures to Natural Communities

Published on: July 12, 2018

12.6K
Efficient Nucleic Acid Extraction and 16S rRNA Gene Sequencing for Bacterial Community Characterization
12:37

Efficient Nucleic Acid Extraction and 16S rRNA Gene Sequencing for Bacterial Community Characterization

Published on: April 14, 2016

40.7K

Area of Science:

  • Microbiology
  • Computational Biology
  • Bioinformatics

Background:

  • Microbiome samples are complex mixtures of numerous species with sparse abundance data.
  • Classical methods struggle to identify intricate patterns in such data.
  • Identifying microbial community structure is challenging due to high dimensionality and rare species.

Purpose of the Study:

  • To introduce BioMiCo, a novel hierarchical Bayesian model for microbial community analysis.
  • To develop a supervised learning framework for predicting sample features from microbiome data.
  • To address challenges posed by sparse data, numerous variables, and rare species in microbiome research.

Main Methods:

  • Developed a two-level hierarchical Bayesian model (BioMiCo) using Dirichlet priors.
  • Employed supervised learning by training the model on known sample features and abundance data.
  • Utilized environmental DNA (eDNA) abundance data for microbial community composition modeling.

Main Results:

  • BioMiCo accurately predicted body site and host identity across different time points.
  • Distinct microbiome structures were identified for different Nugent scores in vaginal samples.
  • The model successfully tracked seasonal transitions and predicted ecosystem events in a coastal bacterial community.

Conclusions:

  • BioMiCo offers a robust framework for understanding microbial community structure and dynamics.
  • The model enables accurate predictions of sample features and community transitions.
  • BioMiCo can be applied to diverse microbial communities by incorporating relevant biotic or abiotic features.