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

Sensitivity, Specificity, and Predicted Value01:13

Sensitivity, Specificity, and Predicted Value

764
In healthcare diagnostics, laboratory tests play a crucial role in identifying and diagnosing a wide range of medical conditions. However, interpreting test results is not always straightforward. An abnormal test result does not always confirm the presence of a disease, just as a normal result does not guarantee its absence. To assess the reliability of these diagnostic tools, healthcare practitioners rely on two key statistical indicators: sensitivity and specificity.
Sensitivity is the...
764
Steps in Outbreak Investigation01:18

Steps in Outbreak Investigation

249
In the ever-evolving field of public health, statistical analysis serves as a cornerstone for understanding and managing disease outbreaks. By leveraging various statistical tools, health professionals can predict potential outbreaks, analyze ongoing situations, and devise effective responses to mitigate impact. For that to happen, there are a few possible stages of the analysis:
249
Survival Tree01:19

Survival Tree

178
Survival trees are a non-parametric method used in survival analysis to model the relationship between a set of covariates and the time until an event of interest occurs, often referred to as the "time-to-event" or "survival time." This method is particularly useful when dealing with censored data, where the event has not occurred for some individuals by the end of the study period, or when the exact time of the event is unknown.
 Building a Survival Tree
Constructing a...
178
Factors Affecting the Risk of Infection01:26

Factors Affecting the Risk of Infection

12.7K
The hosts' susceptibility to infection depends on several factors. The integrity of the skin and mucous membranes helps protect the body against microbial attacks. When the skin is altered, the chance of infection, limb loss, and even death increases.
The integrity and count of the white blood cells help the body resist pathogens and fight infection. When impaired, it reduces the body's resistance to pathogens. The acidic pH levels of the gastrointestinal, genitourinary tracts, and skin...
12.7K
Predicting Reaction Outcomes02:24

Predicting Reaction Outcomes

8.7K
Kinetics describes the rate and path by which a reaction occurs. In contrast, thermodynamics deals with state functions and describes the properties, behavior, and components of a system. It is not concerned with the path taken by the process and cannot address the rate at which a reaction occurs. Although it does provide information about what can happen during a reaction process, it does not describe the detailed steps of what appears on an atomic or a molecular level. On the other hand,...
8.7K
Random and Systematic Errors01:20

Random and Systematic Errors

13.2K
Scientists always try their best to record measurements with the utmost accuracy and precision. However, sometimes errors do occur. These errors can be random or systematic. Random errors are observed due to the inconsistency or fluctuation in the measurement process, or variations in the quantity itself that is being measured. Such errors fluctuate from being greater than or less than the true value in repeated measurements. Consider a scientist measuring the length of an earthworm using a...
13.2K

You might also read

Related Articles

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

Sort by
Same author

Nanoscale Activity Mapping of Chloride-Permeable Pentameric Receptors.

ACS sensors·2026
Same author

Clinical and Molecular Genetic Characterization of Landau Kleffner Syndrome: An Observational Cohort and Experimental Study.

Annals of neurology·2025
Same author

Propolis compound inhibits profibrotic TGF-β1/SMAD signalling in human fibroblasts.

Scientific reports·2025
Same author

Milestone Review: Unlocking the Proteomics of Glycine Receptor Complexes.

Journal of neurochemistry·2025
Same author

The emerging role of glycine receptor α2 subunit defects in neurodevelopmental disorders.

Frontiers in molecular neuroscience·2025
Same author

Biallelic <i>SLC13A1</i> loss-of-function variants result in impaired sulfate transport and skeletal phenotypes, including short stature, scoliosis, and skeletal dysplasia.

Genetics in medicine open·2025

Related Experiment Video

Updated: Oct 7, 2025

Comparison of Predictive Performance of Three Lymph Node Staging Systems in Colorectal Signet Ring Cell Carcinoma Based on Machine Learning Model
07:13

Comparison of Predictive Performance of Three Lymph Node Staging Systems in Colorectal Signet Ring Cell Carcinoma Based on Machine Learning Model

Published on: April 18, 2025

241

Lessons Learnt From Using the Machine Learning Random Forest Algorithm to Predict Virulence in Streptococcus

Sean J Buckley1, Robert J Harvey1,2

  • 1School of Health and Behavioural Sciences, University of the Sunshine Coast, Maroochydore DC, QLD, Australia.

Frontiers in Cellular and Infection Microbiology
|January 10, 2022
PubMed
Summary
This summary is machine-generated.

Group A Streptococcus (GAS) genotype-phenotype association studies are crucial for understanding this pathogen. This review highlights challenges and proposes a novel workflow using machine learning for improved bacterial genome analysis.

Keywords:
Streptococcus pyogenesmachine learningphenotype metadatarandom forestvirulence

More Related Videos

Heuristic Mining of Hierarchical Genotypes and Accessory Genome Loci in Bacterial Populations
08:03

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

Published on: December 7, 2021

2.5K
Multi-locus Variable-number Tandem-repeat Analysis of the Fish-pathogenic Bacterium Yersinia ruckeri by Multiplex PCR and Capillary Electrophoresis
10:33

Multi-locus Variable-number Tandem-repeat Analysis of the Fish-pathogenic Bacterium Yersinia ruckeri by Multiplex PCR and Capillary Electrophoresis

Published on: June 17, 2019

10.9K

Related Experiment Videos

Last Updated: Oct 7, 2025

Comparison of Predictive Performance of Three Lymph Node Staging Systems in Colorectal Signet Ring Cell Carcinoma Based on Machine Learning Model
07:13

Comparison of Predictive Performance of Three Lymph Node Staging Systems in Colorectal Signet Ring Cell Carcinoma Based on Machine Learning Model

Published on: April 18, 2025

241
Heuristic Mining of Hierarchical Genotypes and Accessory Genome Loci in Bacterial Populations
08:03

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

Published on: December 7, 2021

2.5K
Multi-locus Variable-number Tandem-repeat Analysis of the Fish-pathogenic Bacterium Yersinia ruckeri by Multiplex PCR and Capillary Electrophoresis
10:33

Multi-locus Variable-number Tandem-repeat Analysis of the Fish-pathogenic Bacterium Yersinia ruckeri by Multiplex PCR and Capillary Electrophoresis

Published on: June 17, 2019

10.9K

Area of Science:

  • Microbiology
  • Genomics
  • Computational Biology

Background:

  • Group A Streptococcus (GAS) is a major global human pathogen with significant genomic variability.
  • Understanding the relationship between GAS genotypes and phenotypes is essential for controlling infections.

Purpose of the Study:

  • To review the application of genotype-phenotype association studies in GAS using whole-genome sequencing.
  • To identify bottlenecks and propose solutions for applying genome-wide association studies (GWAS) and machine learning (ML) in bacterial genomics.

Main Methods:

  • Catalogued transcription regulators in GAS.
  • Employed phylogenetics, concordance metrics, and machine learning (ML) for association testing.
  • Reviewed existing GWAS and ML studies in bacterial genotype-phenotype research.

Main Results:

  • GAS genome variability makes it suitable for genotype-phenotype studies.
  • Existing GWAS methods need adaptation for bacterial genomes.
  • A deficit exists in user-friendly workflows and detailed clinical metadata.

Conclusions:

  • GWAS and ML hold promise for bacterial genotype-phenotype association studies.
  • A novel quality control protocol and workflow are proposed for GAS virulence phenotype and clinical outcome data.
  • Linked patient-microbe genome sets are recommended to better represent infection events.