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

Modern Molecular Taxonomy01:29

Modern Molecular Taxonomy

352
Advancements in molecular biology have revolutionized the identification and characterization of bacteria, with multiple methods leveraging DNA sequencing for enhanced precision. As sequencing technologies improve and costs decline, these approaches are increasingly used in clinical, environmental, and evolutionary studies.Multilocus Sequence Typing (MLST) examines several housekeeping genes, essential chromosomal genes encoding cellular functions, to distinguish strains. Approximately...
352
Microbial Classification System01:24

Microbial Classification System

496
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...
496
Applications of Molecular Taxonomy01:20

Applications of Molecular Taxonomy

292
Molecular taxonomy has revolutionized the understanding and classification of bacteria, providing precise insights into their diversity, evolutionary relationships, and ecological roles. By utilizing molecular techniques such as DNA sequencing and fingerprinting, researchers have made significant strides in various fields related to bacterial studies.Resolving Taxonomic AmbiguitiesMolecular taxonomy has been instrumental in distinguishing closely related bacterial species initially thought to...
292
Methods of Classification and Identification01:28

Methods of Classification and Identification

602
Bacterial identification relies on a diverse array of techniques to classify and understand microorganisms, each tailored to uncover specific characteristics. Traditional morphological approaches, while still valuable, are limited for closely related or structurally simple organisms. Modern methods integrate biochemical, serological, genetic, and advanced molecular tools to achieve greater accuracy.Morphological and Biochemical TechniquesMorphological characteristics, such as cell shape and...
602
Model Approaches for Pharmacokinetic Data: Distributed Parameter Models01:06

Model Approaches for Pharmacokinetic Data: Distributed Parameter Models

164
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...
164

You might also read

Related Articles

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

Sort by
Same author

Genome-wide association study of early-stage non-small cell lung cancer prognosis: a pooled analysis in the International Lung Cancer Consortium.

Carcinogenesis·2025
Same author

Cell-specific occupancy dynamics between the pioneer-like factor Opa/ZIC and Ocelliless/OTX regulate early head development in embryos.

Frontiers in cell and developmental biology·2023
Same author

Review of the Chinese species of the genus Scelimena Serville, 1838 (Tetrigidae: Scelimeninae: Scelimenini).

Zootaxa·2023
Same author

Redox Neutral Radical-Relay Nickel-Catalyzed Remote Carbonylation.

Organic letters·2023
Same author

Association between the triglyceride-glucose index and cognitive impairment in China: a community population-based cross-sectional study.

Nutritional neuroscience·2023
Same author

Characteristics and prognosis of rrDLBCL with TP53 mutations and a high-risk subgroup represented by the co-mutations of DDX3X-TP53.

Cancer medicine·2023

Related Experiment Video

Updated: Nov 13, 2025

Microbiota Analysis Using Two-step PCR and Next-generation 16S rRNA Gene Sequencing
11:22

Microbiota Analysis Using Two-step PCR and Next-generation 16S rRNA Gene Sequencing

Published on: October 15, 2019

30.6K

DCMD: Distance-based classification using mixture distributions on microbiome data.

Konstantin Shestopaloff1, Mei Dong1, Fan Gao1

  • 1Dalla Lana School of Public Health, University of Toronto, Toronto, Ontario, CANADA.

Plos Computational Biology
|March 12, 2021
PubMed
Summary
This summary is machine-generated.

This study introduces a new method for classifying microbiome data, which is often sparse and uneven. The distance-based classification using mixture distributions (DCMD) method improves accuracy for human disease research.

More Related Videos

Divergence of Root Microbiota in Different Habitats based on Weighted Correlation Networks
09:49

Divergence of Root Microbiota in Different Habitats based on Weighted Correlation Networks

Published on: September 25, 2021

4.6K
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

16.6K

Related Experiment Videos

Last Updated: Nov 13, 2025

Microbiota Analysis Using Two-step PCR and Next-generation 16S rRNA Gene Sequencing
11:22

Microbiota Analysis Using Two-step PCR and Next-generation 16S rRNA Gene Sequencing

Published on: October 15, 2019

30.6K
Divergence of Root Microbiota in Different Habitats based on Weighted Correlation Networks
09:49

Divergence of Root Microbiota in Different Habitats based on Weighted Correlation Networks

Published on: September 25, 2021

4.6K
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

16.6K

Area of Science:

  • Microbiology
  • Bioinformatics
  • Computational Biology

Background:

  • Next-generation sequencing enables microbiome research linking the microbiome to chronic diseases.
  • Microbiome data's sparsity and skewness challenge classifier development.
  • Existing methods struggle with the unique characteristics of microbiome data.

Purpose of the Study:

  • To develop an innovative distance-based classification method (DCMD) for sparse and heterogeneous microbiome count data.
  • To improve classification performance in microbiome-associated health outcome studies.
  • To address the inherent uncertainty in sparse microbiome data.

Main Methods:

  • Proposed distance-based classification using mixture distributions (DCMD).
  • Modeled uncertainty in sparse counts using mixture distributions.
  • Implemented DCMD within k-means and k-nearest neighbours frameworks with novel distance metrics.
  • Evaluated performance on simulated and human microbiome data.

Main Results:

  • The DCMD method demonstrated competitive performance against existing machine learning approaches.
  • DCMD showed significant improvement over traditional distance-based classifiers.
  • The importance of modeling data sparsity for optimal microbiome classification was highlighted.

Conclusions:

  • The DCMD method offers a robust and applicable alternative for classifying sparse microbiome count data.
  • Accurate classification of microbiome data is crucial for understanding human disease.
  • The developed approach enhances the analysis of microbiome-associated health outcomes.