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

Classification of Signals01:30

Classification of Signals

1.6K
In signal processing, signals are classified based on various characteristics: continuous-time versus discrete-time, periodic versus aperiodic, analog versus digital, and causal versus noncausal. Each category highlights distinct properties crucial for understanding and manipulating signals.
A continuous-time signal holds a value at every instant in time, representing information seamlessly. In contrast, a discrete-time signal holds values only at specific moments, often denoted as x(n), where...
1.6K
Methods of Classification and Identification01:28

Methods of Classification and Identification

1.6K
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...
1.6K
Force Classification01:22

Force Classification

2.6K
Forces play a crucial role in the study of physics and engineering. They are essential in describing the motion, behavior, and equilibrium of objects in the physical world. Forces can be classified based on their origin, type, and direction of action.
Contact and non-contact forces are two of the most widely used categories of forces. As the name suggests, contact forces require physical contact between two objects to act upon each other. Examples of contact forces include frictional,...
2.6K
Classification of Systems-I01:26

Classification of Systems-I

652
Linearity is a system property characterized by a direct input-output relationship, combining homogeneity and additivity.
Homogeneity dictates that if an input x(t) is multiplied by a constant c, the output y(t) is multiplied by the same constant. Mathematically, this is expressed as:
652
Classification of Skeletal Muscle Fibers01:48

Classification of Skeletal Muscle Fibers

60.1K
Skeletal muscles continuously produce ATP to provide the energy that enables muscle contractions. Skeletal muscle fibers can be categorized into three types based on differences in their contraction speed and how they produce ATP, as well as physical differences related to these factors. Most human muscles contain all three muscle fiber types, albeit in varying proportions.
Slow-Twitch Muscle Fibers
Slow oxidative, muscle fibers appear red due to large numbers of capillaries and high levels of...
60.1K
Aggregates Classification01:29

Aggregates Classification

1.1K
Aggregate classification is generally based on its size, petrographic characteristics, weight, and source. Size classification ranges from coarse to fine aggregates, defined by the size of the particles. Coarse aggregates are particles that do not pass through ASTM sieve No. 4, and aggregates that pass through the sieve are fine aggregates.
Petrographic classification groups aggregates based on common mineralogical characteristics. Some of the common mineral groups found in aggregates are...
1.1K

You might also read

Related Articles

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

Sort by
Same author

Protocol for genotyping cephalopod sex using a skin swab and quantitative PCR.

STAR protocols·2026
Same author

MKado: a toolkit for McDonald-Kreitman tests of natural selection.

G3 (Bethesda, Md.)·2026
Same author

Ecotypes, <i>Wolbachia</i>, and urbanization shape <i>Culex pipiens</i> population structure in a West Nile virus hotspot.

bioRxiv : the preprint server for biology·2026
Same author

Diversity and divergence of two sympatric, sibling octopus species.

bioRxiv : the preprint server for biology·2026
Same author

A non-invasive method to genotype cephalopod sex by quantitative PCR.

iScience·2026
Same author

Neural posterior estimation for population genetics.

Genetics·2026

Related Experiment Video

Updated: Mar 24, 2026

Author Spotlight: Efficient Image Recognition Using Directional Gradient Histogram Technique and Support Vector Machines
08:27

Author Spotlight: Efficient Image Recognition Using Directional Gradient Histogram Technique and Support Vector Machines

Published on: January 5, 2024

1.7K

S/HIC: Robust Identification of Soft and Hard Sweeps Using Machine Learning.

Daniel R Schrider1, Andrew D Kern1,2

  • 1Department of Genetics, Rutgers University, Piscataway, New Jersey, United States of America.

Plos Genetics
|March 16, 2016
PubMed
Summary

A new method, S/HIC, accurately detects adaptive natural selection (selective sweeps) in whole genome data. It performs well even with complex population histories and model inaccuracies.

More Related Videos

Asthma Detection Research Based on Voice Signal Processing and Machine Learning
04:04

Asthma Detection Research Based on Voice Signal Processing and Machine Learning

Published on: July 22, 2025

1.2K
Machine Learning-Based Cough Tone Classification: Diagnostic Exploration of Chronic Obstructive Pulmonary Disease and Respiratory Tract Infections
06:22

Machine Learning-Based Cough Tone Classification: Diagnostic Exploration of Chronic Obstructive Pulmonary Disease and Respiratory Tract Infections

Published on: September 19, 2025

623

Related Experiment Videos

Last Updated: Mar 24, 2026

Author Spotlight: Efficient Image Recognition Using Directional Gradient Histogram Technique and Support Vector Machines
08:27

Author Spotlight: Efficient Image Recognition Using Directional Gradient Histogram Technique and Support Vector Machines

Published on: January 5, 2024

1.7K
Asthma Detection Research Based on Voice Signal Processing and Machine Learning
04:04

Asthma Detection Research Based on Voice Signal Processing and Machine Learning

Published on: July 22, 2025

1.2K
Machine Learning-Based Cough Tone Classification: Diagnostic Exploration of Chronic Obstructive Pulmonary Disease and Respiratory Tract Infections
06:22

Machine Learning-Based Cough Tone Classification: Diagnostic Exploration of Chronic Obstructive Pulmonary Disease and Respiratory Tract Infections

Published on: September 19, 2025

623

Area of Science:

  • Population genetics
  • Genomics
  • Evolutionary biology

Background:

  • Detecting adaptive natural selection from whole genome sequencing data is crucial for understanding evolution.
  • Existing methods often struggle with realistic demographic scenarios and inferring selection from standing variation.
  • There's a need for robust genome-scale methods to identify selective sweeps.

Purpose of the Study:

  • Introduce S/HIC, a novel supervised machine learning method for detecting hard and soft selective sweeps.
  • Evaluate S/HIC's accuracy and robustness compared to existing methods.
  • Apply S/HIC to human genomic data to identify past selective events.

Main Methods:

  • Developed S/HIC, a supervised machine learning approach.
  • Tested S/HIC's performance on simulated data with realistic human demographic histories.
  • Compared S/HIC against other sweep detection methods.
  • Applied S/HIC to human chromosome 18 resequencing data.

Main Results:

  • S/HIC demonstrates unrivaled accuracy in detecting selective sweeps under relevant demographic histories.
  • The method effectively distinguishes sweeps from linked and neutral regions.
  • S/HIC shows unique robustness to demographic model misspecification.
  • Successfully identified known selective sweeps in human chromosome 18 data.

Conclusions:

  • S/HIC is a highly accurate and robust tool for detecting selective sweeps from whole genome data.
  • The method performs well even when population demographic models are not perfectly specified.
  • S/HIC offers a significant advancement for studying adaptation in natural populations, including humans.