Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Related Concept Videos

Modern Molecular Taxonomy

Modern Molecular Taxonomy

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...

Microbial Growth Measurement: Indirect Methods

Microbial Growth Measurement: Indirect Methods

Estimating microbial growth is essential for understanding population dynamics and environmental adaptations. Indirect methods provide valuable insights by measuring parameters such as turbidity, metabolic activity, and biomass, enabling efficient and reproducible assessments.During exponential growth, microbial cells scatter light proportionally to their biomass, a principle used in turbidity measurements. About one million cells per milliliter produce detectable scattering, which a...

Evolutionary Relationships through Genome Comparisons

Evolutionary Relationships through Genome Comparisons

Genome comparison is one of the excellent ways to interpret the evolutionary relationships between organisms. The basic principle of genome comparison is that if two species share a common feature, it is likely encoded by the DNA sequence conserved between both species. The advent of genome sequencing technologies in the late 20th century enabled scientists to understand the concept of conservation of domains between species and helped them to deduce evolutionary relationships across diverse...

Applications of Molecular Taxonomy

Applications of Molecular Taxonomy

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...

Mechanistic Models: Compartment Models in Individual and Population Analysis

Mechanistic Models: Compartment Models in Individual and Population Analysis

Mechanistic models are utilized in individual analysis using single-source data, but imperfections arise due to data collection errors, preventing perfect prediction of observed data. The mathematical equation involves known values (Xi), observed concentrations (Ci), measurement errors (εi), model parameters (ϕj), and the related function (ƒi) for i number of values. Different least-squares metrics quantify differences between predicted and observed values. The ordinary least...

What is Population Genetics?

What is Population Genetics?

A population is composed of members of the same species that simultaneously live and interact in the same area. When individuals in a population breed, they pass down their genes to their offspring. Many of these genes are polymorphic, meaning that they occur in multiple variants. Such variations of a gene are referred to as alleles. The collective set of all the alleles within a population is known as the gene pool.

You might also read

Related Articles

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

Sort by

Same author

An antibiotic chatbot: Evaluation of a retrieval-augmented generation approach for providing guideline-based antimicrobial advice.

The Journal of infection·2026

Same author

Multicenter Validation of Clinical Sepsis Phenotypes.

JAMA network open·2026

Same author

Rapid diagnosis of common, undetected, and uncultivable bloodstream infections from positive blood cultures using Oxford Nanopore sequencing: a metagenomic pipeline analysis.

The Lancet. Microbe·2026

Same author

Engineering an Optogenetic pH-Modulator in Bacteria.

Advanced science (Weinheim, Baden-Wurttemberg, Germany)·2026

Same author

Day-to-day fluctuations in cognitive precision predict the domain-general intention-behavior gap.

Science advances·2026

Same author

How ready are we to use artificial intelligence in our fight against antimicrobial resistance? An ESGAID and EAAS perspective.

Expert review of anti-infective therapy·2026

Same journal

A longitudinal single-nucleus transcriptomic atlas of bovine placentation reveals dynamic cellular hierarchies and regulatory programs.

Genome biology·2026

Same journal

Compound models and Pearson residuals for single-cell RNA-seq data without UMIs.

Genome biology·2026

Same journal

ResSAT: enhancing spatial transcriptomics prediction from H&E-stained histology images with an interactive spot transformer.

Genome biology·2026

Same journal

Srr2-dependent SOX2 levels govern the chromatin and transcriptional landscape of adult neural stem cell fate decisions in mouse.

Genome biology·2026

Same journal

Tumoral switch in NUMB splicing changes essential transcription pathways and induces malignant properties in tumour cells.

Genome biology·2026

Same journal

MLMarker: a machine learning framework for tissue inference and biomarker discovery.

Genome biology·2026

See all related articles

Search research articles

Related Experiment Video

Updated: Jan 6, 2026

Heuristic Mining of Hierarchical Genotypes and Accessory Genome Loci in Bacterial Populations

Heuristic Mining of Hierarchical Genotypes and Accessory Genome Loci in Bacterial Populations

Published on: December 7, 2021

Machine learning and statistical inference in microbial population genomics.

Samuel K Sheppard¹, Nicolas Arning², David W Eyre^2,3,4

¹Ineos Oxford Institute for Antimicrobial Research, Department of Biology, University of Oxford, Oxford, United Kingdom.

|September 27, 2025

Summary

This summary is machine-generated.

Machine learning and statistical inference offer complementary approaches for analyzing large microbial genomics datasets. Combining these methods enhances pathogen research in the big data era.

More Related Videos

Following the Dynamics of Structural Variants in Experimentally Evolved Populations

Following the Dynamics of Structural Variants in Experimentally Evolved Populations

Published on: February 3, 2023

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

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

Published on: May 16, 2022

Related Experiment Videos

Last Updated: Jan 6, 2026

Heuristic Mining of Hierarchical Genotypes and Accessory Genome Loci in Bacterial Populations

Heuristic Mining of Hierarchical Genotypes and Accessory Genome Loci in Bacterial Populations

Published on: December 7, 2021

Following the Dynamics of Structural Variants in Experimentally Evolved Populations

Following the Dynamics of Structural Variants in Experimentally Evolved Populations

Published on: February 3, 2023

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

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

Published on: May 16, 2022

Area of Science:

Microbiology
Genomics
Bioinformatics

Background:

Large genome datasets are transforming microbiology research.
Computational analysis of these datasets is complex.
Machine learning and statistical inference are key data analysis approaches.

Purpose of the Study:

To review the distinct aims and methods of machine learning and statistical inference.
To highlight their complementarity in microbial genomics.
To advocate for their combined use in pathogen research.

Main Methods:

Review of machine learning and statistical inference methodologies.
Application examples from microbial genomics.
Discussion of synthesis and complementarity.

Main Results:

Machine learning excels at prediction.
Statistical inference focuses on understanding relationships.
Both fields share knowledge discovery goals.

Conclusions:

Machine learning and statistical inference have different strengths but overlapping aims.
Combining these approaches offers significant potential for big data-driven pathogen research.