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相关概念视频

Evolutionary Relationships through Genome Comparisons02:54

Evolutionary Relationships through Genome Comparisons

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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...
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Phylogeny01:23

Phylogeny

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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.
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Phylogenetic Trees03:21

Phylogenetic Trees

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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.
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Gene Evolution - Fast or Slow?02:05

Gene Evolution - Fast or Slow?

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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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The Tree of Life - Bacteria, Archaea, Eukaryotes02:40

The Tree of Life - Bacteria, Archaea, Eukaryotes

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The “tree of life” describes the evolution of life and the evolutionary relationships between organisms. The root of the tree is the common ancestor to all life on Earth. All other species radiate from this point, much like the branches of a tree. The numerous tips of these branches on the tree of life represent every living, or extant, species. Extinct species, which are species that no longer exist, can be found towards the center of the tree. Currently, these organisms, both...
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The Evidence for Evolution02:55

The Evidence for Evolution

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Genetic variations accumulating within populations over generations give rise to biological evolution. Evolutionary changes can result in the formation of novel varieties and entire new species. These changes are responsible for the diverse forms of life inhabiting the planet. The evidence for evolution suggests that all living organisms descended from common ancestors.
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相关实验视频

Updated: Jul 19, 2025

Using Phylogenetic Analysis to Investigate Eukaryotic Gene Origin
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Using Phylogenetic Analysis to Investigate Eukaryotic Gene Origin

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从族系学中深度学习用于多样化分析.

Sophia Lambert1,2, Jakub Voznica3,4, Hélène Morlon1

  • 1Institut de Biologie de l'École Normale Supérieure, École Normale Supérieure, CNRS, INSERM, Université Paris Sciences et Lettres, 46 Rue d'Ulm, 75005 Paris, France.

Systematic biology
|August 9, 2023
PubMed
概括

深度学习模型现在可以从族系学来推断物种多样化动态,与传统方法的准确性相匹配,但速度明显更快. 这种方法加速了新的进化模型的开发和应用.

关键词:
出生死亡模型的模型.卷积神经网络是一种卷积神经网络.深度学习是一种深度学习.多样化的多样化多样化的多样化宏观演变的发生.植物谱系的表示形式.

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科学领域:

  • 进化生物学是进化的生物学.
  • 计算型的遗传学学.
  • 生物信息学是一种生物信息学.

背景情况:

  • 出生死亡 (BD) 模型对于使用族系学研究物种多样化至关重要.
  • 目前的基于概率的推理方法缺乏通用性,并且对于复杂的模型来说可能是计算难以处理的.
  • 深度学习通过学习模拟和模型参数之间的关系提供了一个潜在的解决方案.

研究的目的:

  • 为了适应深度学习的遗传学多样化推断.
  • 通过使用特征数据,将深度学习扩展到依赖状态的多样化模型.
  • 与传统方法相比,评估深度学习推断的准确性和效率.

主要方法:

  • 从病原体生物动力学中调整了一个深度学习方法,用于多样化推断.
  • 训练深度神经网络,在模拟的族系和模型参数之间进行回归.
  • 将该方法应用于时间恒定的同质BD模型和二进制状态特异和灭绝 (BiSSE) 模型.
  • 通过生态特征数据 (种子分散) 重新分析了灵长类的基因组.

主要成果:

  • 深度学习推断的准确性与基于概率的方法相美.
  • 深度学习方法比传统方法快了数量级.
  • 成功推断了对均质BD和BiSSE模型的参数.
  • 在现实世界灵长类动物原生学数据集上验证了该方法.

结论:

  • 深度学习为遗传学多样化推断提供了一个准确而高效的替代方案.
  • 这种方法扩大了推理的适用性,使其适用于复杂的,依赖状态的模型.
  • 深度学习的速度和通用性将促进新型进化模型的开发和部署.