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

5.7K
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...
5.7K
Phylogenetic Trees03:21

Phylogenetic Trees

45.3K
Phylogenetic trees come in many forms. It matters in which sequence the organisms are arranged from the bottom to the top of the tree, but the branches can rotate at their nodes without altering the information. The lines connecting individual nodes can be straight, angled, or even curved.
45.3K
Phylogeny01:23

Phylogeny

43.8K
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.
43.8K
Taxonomy01:31

Taxonomy

73.7K
Taxonomy is the science of defining and naming groups of biological organisms based on shared characteristics. It uses a hierarchy of increasingly inclusive categories with Latin names. The smallest units of taxonomy, species and genus, are used to assign a formal, taxonomic name to each species in a system. This classification system, referred to as binomial nomenclature, was formalized by Carolus Linnaeus in the 18th century.
Hierarchy of Taxonomy
The hierarchy that Carolus Linnaeus first...
73.7K

You might also read

Related Articles

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

Sort by
Same author

Computer Vision for Monitoring Wild Bees and Wasps: A Structured Literature Review.

Ecology and evolution·2026
Same author

Reliable uncertainty estimates in deep learning with efficient Metropolis-Hastings algorithms.

Nature communications·2026
Same author

Model utility and explainability in federated learning - A case study in healthcare using fundus oculi datasets.

Journal of biomedical informatics·2026
Same author

Towards the automatized identification of moss species from their spore morphology.

Annals of botany·2025
Same author

Time to spice-up paleoecological records with bryophyte spores.

Trends in plant science·2025
Same author

Expanding phenological insights: automated phenostage annotation with community science plant images.

International journal of biometeorology·2025

Related Experiment Video

Updated: Jun 19, 2025

Dissection, MicroCT Scanning and Morphometric Analyses of the Baculum
04:32

Dissection, MicroCT Scanning and Morphometric Analyses of the Baculum

Published on: March 19, 2017

7.5K

Inferring Taxonomic Affinities and Genetic Distances Using Morphological Features Extracted from Specimen Images: A

Martin Hofmann1, Steffen Kiel2, Lara M Kösters3

  • 1Data-intensive Systems and Visualization Group (dAI.SY), Technical University Ilmenau, Ilmenau 98693, Germany.

Systematic Biology
|July 24, 2024
PubMed
Summary

Deep learning models can infer evolutionary relationships from bivalve specimen images, correlating visual similarity with genetic data. This approach offers a new method for phylogenetic analysis when molecular data is unavailable.

Keywords:
Bivalvesdeep learningmorphology inferencephylogeneticssimilarity learning

More Related Videos

Quantification of Orofacial Phenotypes in Xenopus
09:26

Quantification of Orofacial Phenotypes in Xenopus

Published on: November 6, 2014

9.7K
Automatic Image Processing to Determine the Community Size Structure of Riverine Macroinvertebrates
08:56

Automatic Image Processing to Determine the Community Size Structure of Riverine Macroinvertebrates

Published on: January 13, 2023

2.2K

Related Experiment Videos

Last Updated: Jun 19, 2025

Dissection, MicroCT Scanning and Morphometric Analyses of the Baculum
04:32

Dissection, MicroCT Scanning and Morphometric Analyses of the Baculum

Published on: March 19, 2017

7.5K
Quantification of Orofacial Phenotypes in Xenopus
09:26

Quantification of Orofacial Phenotypes in Xenopus

Published on: November 6, 2014

9.7K
Automatic Image Processing to Determine the Community Size Structure of Riverine Macroinvertebrates
08:56

Automatic Image Processing to Determine the Community Size Structure of Riverine Macroinvertebrates

Published on: January 13, 2023

2.2K

Area of Science:

  • * Evolutionary biology and systematic biology.
  • * Computational biology and bioinformatics.

Background:

  • * Reconstructing the tree of life and understanding taxon relationships are central to evolutionary biology.
  • * Molecular phylogenetics has driven advances, but molecular data is unavailable for most species.
  • * Specimen images offer a potential data source for phylogenetic inference.

Purpose of the Study:

  • * To explore the applicability of deep learning methods for inferring organism relationships from specimen images.
  • * To assess the correlation between visual similarity and genetic relationships in bivalves.
  • * To develop automated taxon identification systems and novel phylogenetic estimation methods.

Main Methods:

  • * Assembled a large image dataset of 4144 bivalve species with available molecular phylogenetic data and taxonomic hierarchy.
  • * Employed supervised classification with taxonomic hierarchy and genetic distances for multi-level predictions.
  • * Utilized transfer learning and similarity learning for zero-shot identification of unknown species.
  • * Applied unsupervised similarity learning to infer relatedness from images without prior taxonomic knowledge.

Main Results:

  • * Deep learning models achieved nearly 80% accuracy in species-level identification.
  • * Zero-shot learning models identified higher-level taxonomic affinities with 48-67% accuracy.
  • * Unsupervised learning revealed significant correlations between visual appearance and genetic relationships at higher taxonomic levels (family level correlation: 0.78).
  • * Visual similarity correlated with genetic distances, particularly in species-rich orders and subclasses.

Conclusions:

  • * Deep learning methods can effectively infer phylogenetic relationships from specimen images, complementing molecular data.
  • * Visual similarity in bivalve images reflects genetic relationships, especially at higher taxonomic ranks.
  • * This research expands the utility of automated identification systems and provides a novel approach for phylogenetic analysis.