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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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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.
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Genetic transfer occurs when genetic information is passed from one organism to another. It occurs via two mechanisms: vertical gene transfer and horizontal gene transfer. Vertical gene transfer occurs when genetic information is transferred from one generation to the next, which happens much more frequently than horizontal gene transfer. Both sexual and asexual reproduction are forms of vertical gene transfer, where one or more organisms pass some or all of their genome onto their progeny.
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Phylogeny01:23

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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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While every living organism has a genome of some kind (be it RNA, or DNA), there is considerable variation in the sizes of these blueprints. One major factor that impacts genome size is whether the organism is prokaryotic or eukaryotic. In prokaryotes, the genome contains little to no non-coding sequence, such that genes are tightly clustered in groups or operons sequentially along the chromosome. Conversely, the genes in eukaryotes are punctuated by long stretches of non-coding sequence.
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Updated: Jul 17, 2025

Inherent Dynamics Visualizer, an Interactive Application for Evaluating and Visualizing Outputs from a Gene Regulatory Network Inference Pipeline
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使用生成对抗网络进行基系推断.

Megan L Smith1, Matthew W Hahn1,2

  • 1Department of Biology, Indiana University, 1001 E 3rd St, Bloomington, IN 47405, United States.

Bioinformatics (Oxford, England)
|September 5, 2023
PubMed
概括
此摘要是机器生成的。

生成对抗性网络 (GAN) 提供了一种新的机器学习方法来推断进化关系,克服了传统遗传学推断的局限性. 这种方法,phyloGAN,显示出复杂的进化建模的希望.

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科学领域:

  • 计算生物学 计算生物学
  • 进化生物学 进化生物学
  • 机器学习 机器学习

背景情况:

  • 遗传学推断对于理解进化历史至关重要,但由于庞大的模型空间,它面临着计算挑战.
  • 监督机器学习方法在这些空间中需要大量的数据,将其应用限制在更简单的遗传学问题上 (例如,未根植的四重奏).

研究的目的:

  • 探索生成对抗网络 (GAN) 的潜力,以解决基因推理中的计算局限性.
  • 开发和评估一个基于GAN的工具,phyloGAN,用于推断家族遗传关系.

主要方法:

  • 开发了phyloGAN,这是一个利用进化模型作为生成器的GAN框架,用于创建基因推理数据.
  • 在多达15个种类的连接对齐和基因对齐上测试了phyloGAN,评估其推断家族遗传树的能力.
  • 在考虑基因树异质性的情况下和没有考虑基因树异质性的情况下探索推断.

主要成果:

  • 在测试的种群中,phyloGAN在推断家族遗传关系方面表现出相对较低的错误率.
  • 该研究确定了慢运行时间和训练不稳定性作为当前phyloGAN架构的改进领域.
  • 在连接中评估了最多15种类型和在考虑基因树异质性时评估了6种类型的表现.

结论:

  • 生成对抗性网络提供了一种可行的机器学习方法,用于在遗传学中导航复杂的模型空间.
  • phyloGAN为进化推理提供了一个新的计算工具,尽管需要进一步的架构开发以提高效率和稳定性.
  • 该研究强调了GANs在推进基因学中的机器学习应用中的潜力.