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Related Concept Videos

Evolutionary Relationships through Genome Comparisons02:54

Evolutionary Relationships through Genome Comparisons

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...
Synteny and Evolution02:31

Synteny and Evolution

John H. Renwick first coined the term “synteny” in 1971, which refers to the genes present on the same chromosomes, even if they are not genetically linked. The species with common ancestry tend to show conserved syntenic regions. Therefore, the concept of synteny is nowadays used to describe the evolutionary relationship between species.
Around 80 million years ago, the human and mice lineages diverged from the common ancestor. During the course of evolution, the ancestral chromosome underwent...
Convergent Evolution01:54

Convergent Evolution

Evolution shapes the features of organisms over time, ensuring that they are suited for the environments in which they live. Sometimes, selection pressure leads to the rise of similar but unrelated adaptations in organisms with no recent common ancestors, a process known as convergent evolution.The structures that arise from convergent evolution are called analogous structures. They are similar in function even if they are dissimilar in structure. Further, structures can be analogous while also...
Phylogeny01:23

Phylogeny

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

Gene Evolution - Fast or Slow?

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

Gene Evolution - Fast or Slow?

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...

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Updated: Jul 15, 2026

Creating Objects and Object Categories for Studying Perception and Perceptual Learning
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Creating Objects and Object Categories for Studying Perception and Perceptual Learning

Published on: November 2, 2012

Connecting links with code: AI in morphological evolution.

Nimisha Singh1, Pragya Chaturvedi1, Devansh Saxena1

  • 1Department of Biotechnology, Dr. B. Lal Institute of Biotechnology, Jaipur, Rajasthan, India.

Frontiers in Bioinformatics
|July 13, 2026
PubMed
Summary

Artificial intelligence (AI) and machine learning can revolutionize evolutionary biology and paleontology by automating morphological data analysis. These advanced computational tools promise to reduce bias and improve the accuracy of phylogenetic and macroevolutionary studies.

Keywords:
artificial intelligencedeep learningmorphologymorphometric analysisphylogenetics

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Morphological Analysis of Drosophila Larval Peripheral Sensory Neuron Dendrites and Axons Using Genetic Mosaics
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Published on: November 7, 2011

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Last Updated: Jul 15, 2026

Creating Objects and Object Categories for Studying Perception and Perceptual Learning
14:38

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Morphological Analysis of Drosophila Larval Peripheral Sensory Neuron Dendrites and Axons Using Genetic Mosaics
09:42

Morphological Analysis of Drosophila Larval Peripheral Sensory Neuron Dendrites and Axons Using Genetic Mosaics

Published on: November 7, 2011

Area of Science:

  • Evolutionary biology
  • Paleontology
  • Computational biology

Background:

  • Morphological data from fossils are crucial for understanding life's history, guiding evolutionary timelines, and explaining macroevolutionary trends.
  • Traditional methods for analyzing morphological data are subjective and prone to errors, leading to phylogenetic instability.
  • Spatial-temporal fossil data provide insights into biogeography, niche evolution, and adaptation.

Purpose of the Study:

  • To review how Artificial Intelligence (AI) and machine learning can enhance the analysis of morphological data in evolutionary biology and paleontology.
  • To identify technical and methodological gaps in integrating AI/ML into paleontological research.

Main Methods:

  • Review of AI and machine learning applications in processing quantitative shape analysis of morphological data.
  • Identification of automation, bias reduction, and scalability benefits of AI/ML in evolutionary studies.
  • Mapping integration pathways for AI/ML tools into repeatable research workflows.

Main Results:

  • AI and machine learning offer automated, less biased, and scalable methods for morphological data analysis.
  • Quantitative shape analysis powered by AI/ML can overcome limitations of traditional methods.
  • Specific technical and methodological integration strategies have been identified.

Conclusions:

  • AI and machine learning hold significant potential to advance evolutionary biology and paleontology.
  • Integrating these computational tools can lead to more robust and repeatable scientific inferences.
  • Addressing identified gaps will facilitate the adoption of AI/ML in morphological data analysis.