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

Single Nucleotide Polymorphisms-SNPs01:05

Single Nucleotide Polymorphisms-SNPs

14.8K
A single nucleotide polymorphism or SNP is a single nucleotide variation at a specific genomic position in a large population. It is the most prevalent type of sequence variation found in the human genome. Point mutations that occur in more than 1% of the population qualify as SNPs. These are present once every 1000 nucleotides on an average in the human genome. Replacement of a purine with another purine (A/G) or a pyrimidine with another pyrimidine (C/T) is known as a transition. In contrast,...
14.8K
Genome-wide Association Studies-GWAS01:11

Genome-wide Association Studies-GWAS

13.2K
Genome-wide association studies or GWAS are used to identify whether common SNPs are associated with certain diseases. Suppose specific SNPs are more frequently observed in individuals with a particular disease than those without the disease. In that case, those SNPs are said to be associated with the disease. Chi-square analysis is performed to check the probability of the allele likely to be associated with the disease.
GWAS does not require the identification of the target gene involved in...
13.2K
Genetic Screens02:46

Genetic Screens

4.9K
Genetic screens are tools used to identify genes and mutations responsible for phenotypes of interest. Genetic screens help identify individuals or a group of people at risk of developing  genetic diseases and help them with early intervention, targeted therapy, and reproductive options.
Forward genetic screens
Forward or “classical” genetic screens involve creating random mutations in an organism’s DNA using radiation, mutagens, or insertion of additional bases, which...
4.9K

You might also read

Related Articles

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

Sort by
Same author

Clinical Significance of Non-Invasive Skin Autofluorescence Measurement and AI Applications in Patients with Diabetic Foot Ulcers: A Scoping Review.

Journal of personalized medicine·2026
Same author

A Non-Stationary Model for Analysis of Impedance Spectra of Biological Samples.

Entropy (Basel, Switzerland)·2026
Same author

Pain, Opioids, and Functional Connectivity in Preterm Infants.

Children (Basel, Switzerland)·2026
Same author

Optimising Camera-ChArUco Geometry for Motion Compensation in Standing Equine CT: A CT-Motivated Benchtop Study.

Sensors (Basel, Switzerland)·2026
Same author

Listeners' baseline autonomic states associated with distinct music-physiology response patterns.

European heart journal. Imaging methods and practice·2026
Same author

Do Rare Genetic Conditions Exhibit a Specific Phonotype? A Comprehensive Description of the Vocal Traits Associated with Crisponi/Cold-Induced Sweating Syndrome Type 1.

Genes·2025

Related Experiment Video

Updated: Jun 12, 2025

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances
07:35

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances

Published on: October 11, 2018

7.4K

Genetic Algorithms for Feature Selection in the Classification of COVID-19 Patients.

Cosimo Aliani1, Eva Rossi1, Mateusz Soliński2,3

  • 1Department of Information Engineering, University of Florence, 50139 Florence, Italy.

Bioengineering (Basel, Switzerland)
|September 27, 2024
PubMed
Summary

Heart Rate Variability (HRV) analysis from PPG signals, combined with genetic algorithms and machine learning, can identify microcirculation changes in COVID-19 patients. This method effectively distinguishes between healthy individuals and those with mild COVID-19 infection.

Keywords:
COVID-19genetic algorithmsheart rate variabilityphotoplethysmography

More Related Videos

Author Spotlight: Impact of Intergenic Interactions on Disease-Identifying Dark Biomarkers
03:37

Author Spotlight: Impact of Intergenic Interactions on Disease-Identifying Dark Biomarkers

Published on: March 1, 2024

655
Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model
07:15

Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model

Published on: August 16, 2020

6.7K

Related Experiment Videos

Last Updated: Jun 12, 2025

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances
07:35

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances

Published on: October 11, 2018

7.4K
Author Spotlight: Impact of Intergenic Interactions on Disease-Identifying Dark Biomarkers
03:37

Author Spotlight: Impact of Intergenic Interactions on Disease-Identifying Dark Biomarkers

Published on: March 1, 2024

655
Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model
07:15

Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model

Published on: August 16, 2020

6.7K

Area of Science:

  • Cardiovascular Physiology
  • Computational Biology
  • Infectious Disease Dynamics

Background:

  • COVID-19 infection poses risks, including impacts on microcirculatory function.
  • Understanding these microvascular changes is crucial for patient management.

Purpose of the Study:

  • To investigate microcirculation alterations in COVID-19 patients using Heart Rate Variability (HRV) analysis.
  • To identify key HRV parameters indicative of COVID-19-related microcirculatory dysfunction.

Main Methods:

  • Analysis of HRV parameters derived from PhotoPlethysmoGraphy (PPG) signals in 97 subjects (50 healthy, 47 mild COVID-19).
  • Utilized genetic algorithms for feature selection and five machine learning classifiers for discrimination.
  • Investigated 26 HRV parameters to identify significant indicators of infection.

Main Results:

  • Genetic algorithms identified three significant HRV parameters: meanRR, alpha1, and sd2/sd1.
  • A Decision Tree classifier, using these three parameters, achieved 82% accuracy, 86% specificity, and 79% sensitivity.
  • These findings demonstrate the efficacy of selected HRV parameters in differentiating COVID-19 patients.

Conclusions:

  • HRV parameter extraction from PPG signals, coupled with advanced computational methods, effectively identifies microcirculation alterations in COVID-19.
  • This approach aids in distinguishing between healthy individuals and those with mild COVID-19.
  • The study validates the use of machine learning and genetic algorithms for analyzing physiological signals in infectious diseases.