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

Evolutionary Relationships through Genome Comparisons02:54

Evolutionary Relationships through Genome Comparisons

7.2K
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...
7.2K
Gene Evolution - Fast or Slow?02:05

Gene Evolution - Fast or Slow?

8.4K
The genomes of eukaryotes are punctuated by long stretches of sequence which do not code for proteins or RNAs. Although some of these regions do contain crucial regulatory sequences, the vast majority of this DNA serves no known function. Typically, these regions of the genome are the ones in which the fastest change, in evolutionary terms, is observed, because there is typically little to no selection pressure acting on these regions to preserve their sequences.
In contrast, regions which code...
8.4K
Gene Evolution - Fast or Slow?02:05

Gene Evolution - Fast or Slow?

3.8K
3.8K
Microbial Phylogeny01:28

Microbial Phylogeny

61
Understanding the evolutionary relationships among microorganisms is fundamental to microbial ecology and taxonomy. Phylogenetic trees are essential tools for inferring these relationships, relying primarily on comparative analyses of molecular sequences such as DNA, RNA, or proteins. In microbial studies, these trees typically depict the evolutionary paths of diverse bacterial and archaeal species by mapping genetic differences accumulated over time.Phylogenetic trees are composed of tips,...
61
Mutation, Gene Flow, and Genetic Drift01:09

Mutation, Gene Flow, and Genetic Drift

65.8K
In a population that is not at Hardy-Weinberg equilibrium, the frequency of alleles changes over time. Therefore, any deviations from the five conditions of Hardy-Weinberg equilibrium can alter the genetic variation of a given population. Conditions that change the genetic variability of a population include mutations, natural selection, non-random mating, gene flow, and genetic drift (small population size).
65.8K
Applications of Molecular Taxonomy01:20

Applications of Molecular Taxonomy

653
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...
653

You might also read

Related Articles

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

Sort by
Same author

Spatiotemporal patterns and essential role of MSK1 expression after rat spinal cord injury.

Neurochemical research·2013
Same author

Roflumilast for the treatment of COPD in an Asian population: a randomized, double-blind, parallel-group study.

Chest·2013
Same author

JMJD2B promotes epithelial-mesenchymal transition by cooperating with β-catenin and enhances gastric cancer metastasis.

Clinical cancer research : an official journal of the American Association for Cancer Research·2013
Same author

Decreased expression of cystic fibrosis transmembrane conductance regulator impairs sperm quality in aged men.

Reproduction (Cambridge, England)·2013
Same author

[Identification of a novel PAX6 mutation in a family with congenital aniridia].

Zhonghua yi xue yi chuan xue za zhi = Zhonghua yixue yichuanxue zazhi = Chinese journal of medical genetics·2013
Same author

[Effects of adding straw carbon source to root knot nematode diseased soil on soil microbial biomass and protozoa abundance].

Ying yong sheng tai xue bao = The journal of applied ecology·2013
Same journal

Beyond housekeeping: snRNA diversity, regulation, and human disease.

Trends in genetics : TIG·2026
Same journal

Rethinking mitochondrial metabolism: Intraindividual variability meets population constraints.

Trends in genetics : TIG·2026
Same journal

A role for epigenetics in rapid adaptation.

Trends in genetics : TIG·2026
Same journal

The myth of asexual fungi.

Trends in genetics : TIG·2026
Same journal

Rethinking molecular evolution through protein language model embeddings.

Trends in genetics : TIG·2026
Same journal

Co-transcriptional splicing: Distinct phases, mutual benefits, and basis for nuclear architecture.

Trends in genetics : TIG·2026
See all related articles

Related Experiment Video

Updated: Mar 27, 2026

Using Phylogenetic Analysis to Investigate Eukaryotic Gene Origin
08:57

Using Phylogenetic Analysis to Investigate Eukaryotic Gene Origin

Published on: August 14, 2018

16.7K

Machine learning for evolutionary genetics and molecular evolution.

Nicolas Svetec1, UnJin Lee1, Li Zhao1

  • 1Laboratory of Evolutionary Genetics and Genomics, The Rockefeller University, New York, NY 10065, USA.

Trends in Genetics : TIG
|March 25, 2026
PubMed
Summary
This summary is machine-generated.

Machine learning (ML) is transforming biological research, including evolutionary genetics. This review explores ML applications and challenges in understanding genotype, phenotype, and evolutionary history.

Keywords:
artificial intelligencedeep learningmachine learningmolecular evolutionpopulation genetics

More Related Videos

A Bioinformatics Pipeline for Investigating Molecular Evolution and Gene Expression using RNA-seq
07:09

A Bioinformatics Pipeline for Investigating Molecular Evolution and Gene Expression using RNA-seq

Published on: May 28, 2021

10.7K
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

3.1K

Related Experiment Videos

Last Updated: Mar 27, 2026

Using Phylogenetic Analysis to Investigate Eukaryotic Gene Origin
08:57

Using Phylogenetic Analysis to Investigate Eukaryotic Gene Origin

Published on: August 14, 2018

16.7K
A Bioinformatics Pipeline for Investigating Molecular Evolution and Gene Expression using RNA-seq
07:09

A Bioinformatics Pipeline for Investigating Molecular Evolution and Gene Expression using RNA-seq

Published on: May 28, 2021

10.7K
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

3.1K

Area of Science:

  • Evolutionary biology
  • Genetics
  • Computational biology

Background:

  • Large-scale data and computational power have driven machine learning (ML) advances.
  • ML, particularly deep learning, is reshaping biological research.
  • Evolutionary genetics and molecular evolution are areas ripe for ML-driven transformation.

Purpose of the Study:

  • To review key advances in applying ML to genetics and evolution.
  • To discuss ongoing challenges in ML for evolutionary studies.
  • To highlight AI's potential in connecting genotype, phenotype, and evolutionary history.

Main Methods:

  • Review of current literature on machine learning applications in evolutionary genetics.
  • Analysis of challenges and limitations in current ML methodologies.
  • Exploration of artificial intelligence (AI) capabilities for biological data integration.

Main Results:

  • ML is increasingly applied across various biological research domains.
  • Significant challenges remain in integrating ML with complex evolutionary datasets.
  • AI offers promising avenues for linking genetic information with observable traits and evolutionary trajectories.

Conclusions:

  • Machine learning presents a transformative opportunity for evolutionary genetics.
  • Addressing current challenges is crucial for fully realizing ML's potential in the field.
  • AI integration can provide novel insights into the genotype-phenotype-evolution relationship.