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

What is Evolutionary History?02:35

What is Evolutionary History?

43.3K
Scientists record evolutionary history by analyzing fossil, morphological, and genetic data. The fossil record documents the history of life on Earth and provides evidence for evolution. However, both fossil and living organisms offer evidence that outlines Earth’s evolutionary history.
43.3K
Phylogeny01:23

Phylogeny

60.3K
Phylogeny is concerned with the evolutionary diversification of organisms or groups of organisms. A group of organisms with a name is called a taxon (singular). Taxa (plural) can span different levels of the evolutionary hierarchy. For instance, the group containing all birds is a taxon (comprising the class Aves), and the group of all species of daisies (the genus Bellis) is a taxon. Phylogenies can likewise include just one genus (i.e., depict species relationships) or span an entire kingdom.
60.3K
Speciation Rates01:07

Speciation Rates

22.8K
Overview
22.8K
Evolutionary Psychology01:20

Evolutionary Psychology

1000
Evolutionary psychology explores the origins of human behavior and mental processes by framing them within the context of natural selection, a theory famously propounded by Charles Darwin. This field asserts that many behaviors common across human societies — ranging from instinctive fear reactions to complex social interactions — arose as evolutionary adaptations. These adaptations enhanced the survival and reproductive success of our ancestors, thereby becoming embedded in the...
1000
Criticisms of the Evolutionary Perspective01:23

Criticisms of the Evolutionary Perspective

372
In a study where individuals posing as strangers offered compliments and proposed casual sex to students, the responses differed significantly based on gender. Not a single woman accepted the proposal, while 70% of the men agreed. This outcome provides a useful scenario to explore through the lens of evolutionary psychology and social learning theory, highlighting the diverse perspectives on human sexual behaviors.
Evolutionary psychology provides one explanation for these findings, suggesting...
372
Machines01:19

Machines

577
Machines are complex structures consisting of movable, pin-connected multi-force members that work together to transmit forces. One example of a machine is the cutting plier, which is used to cut wires by applying forces to its handles. When equal and opposite forces are exerted on the handles of the cutting plier, they cause the cutting edges to come together and apply equal and opposite reaction forces on the wire, which are greater than the applied forces.
A free-body diagram of the...
577

You might also read

Related Articles

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

Sort by
Same author

Tiny Subsamples and Upsampling Tame Big Data Evolutionary Analysis in Phylogenomics.

bioRxiv : the preprint server for biology·2026
Same author

Universality and diversity of gene expression patterns in response to cold acclimation in Drosophila albomicans.

Genes & genetic systems·2026
Same author

MEGA 12.1: Cross-Platform Release for macOS and Linux Operating Systems.

Journal of molecular evolution·2025
Same author

Evolutionary sparse learning reveals the shared genetic basis of convergent traits.

Nature communications·2025
Same author

R3F: An R package for evolutionary dates, rates, and priors using the relative rate framework.

ArXiv·2025
Same author

Evolutionary sparse learning with paired species contrast reveals the shared genetic basis of convergent traits.

bioRxiv : the preprint server for biology·2025

Related Experiment Video

Updated: Jan 30, 2026

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

7.5K

A Machine Learning Method for Detecting Autocorrelation of Evolutionary Rates in Large Phylogenies.

Qiqing Tao1,2, Koichiro Tamura3,4, Fabia U Battistuzzi5

  • 1Institute for Genomics and Evolutionary Medicine, Temple University, Philadelphia, PA.

Molecular Biology and Evolution
|January 29, 2019
PubMed
Summary

Evolutionary rates are autocorrelated across the tree of life, meaning related species evolve similarly. A new machine learning method, CorrTest, detects this common phenomenon in DNA and amino acid sequences.

Keywords:
TimeTreephylogenomicsrate autocorrelation

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

1.0K
Constructing and Visualizing Models using Mime-based Machine-learning Framework
06:19

Constructing and Visualizing Models using Mime-based Machine-learning Framework

Published on: July 22, 2025

2.5K

Related Experiment Videos

Last Updated: Jan 30, 2026

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

7.5K
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

1.0K
Constructing and Visualizing Models using Mime-based Machine-learning Framework
06:19

Constructing and Visualizing Models using Mime-based Machine-learning Framework

Published on: July 22, 2025

2.5K

Area of Science:

  • Evolutionary Biology
  • Genomics
  • Bioinformatics

Background:

  • Speciation predicts similar evolutionary rates between ancestors and descendants, leading to rate autocorrelation.
  • Previous molecular data analyses have not confirmed this expectation, potentially due to limitations in detection methods.

Purpose of the Study:

  • To introduce CorrTest, a novel machine learning method for detecting rate autocorrelation in large phylogenies.
  • To assess the prevalence of rate autocorrelation across diverse taxa.

Main Methods:

  • Developed CorrTest, a computationally efficient machine learning approach.
  • Applied CorrTest to DNA and amino acid sequence data from various organisms.

Main Results:

  • CorrTest demonstrates superior performance compared to existing methods.
  • Extensive rate autocorrelation was detected in mammals, birds, insects, metazoans, plants, fungi, parasitic protozoans, and prokaryotes.
  • Rate autocorrelation is a widespread phenomenon across the tree of life.

Conclusions:

  • The findings suggest a concordance between molecular and non-molecular evolutionary patterns.
  • Rate autocorrelation is a common evolutionary pattern, impacting phylogenetic analyses.
  • This study will facilitate more accurate dating of the tree of life.