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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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What is Evolutionary History?02:35

What is Evolutionary History?

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Scientists record evolutionary history by analyzing fossil, morphological, and genetic data. The fossil record documents the history of life on Earth and provides evidence for evolution. However, both fossil and living organisms offer evidence that outlines Earth’s evolutionary history.
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Extraction: Advanced Methods00:56

Extraction: Advanced Methods

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Metal ions can be separated from one another by complexation with organic ligands–the chelating agent– to form uncharged chelates. Here, the chelating agent must contain hydrophobic groups and behave as a weak acid, losing a proton to bind with the metal. Since most organic ligands used in this process are insoluble or undergo oxidation in the aqueous phase, the chelating agent is initially added to the organic phase and extracted into the aqueous phase. The metal-ligand complex is...
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Reconstruction of Signal using Interpolation01:10

Reconstruction of Signal using Interpolation

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Signal processing techniques are essential for accurately converting continuous signals to digital formats and vice versa. When a continuous signal is sampled with a period T, the resulting sampled signal exhibits replicas of the original spectrum in the frequency domain, spaced at intervals equal to the sampling frequency. To handle this sampled signal, a zero-order hold method can be applied, which creates a piecewise constant signal by retaining each sample's value until the next...
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Horizontal Gene Transfer01:27

Horizontal Gene Transfer

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Horizontal gene transfer (HGT) is a process where genetic material moves between organisms within the same generation, unlike vertical gene transfer, which occurs from parent to offspring. HGT plays a crucial role in microbial evolution, adaptation, and survival, particularly in shared environments like the human gut.Mobile genetic elements such as plasmids, prophages, integrons, insertion sequences, and transposons facilitate this process. HGT occurs through three primary mechanisms:...
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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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相关实验视频

Updated: Jul 28, 2025

A Virtual Machine Platform for Non-Computer Professionals for Using Deep Learning to Classify Biological Sequences of Metagenomic Data
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通过深度学习推断历史内入.

Yubo Zhang1, Qingjie Zhu2, Yi Shao2

  • 1State Key Laboratory of Protein and Plant Gene Research, Peking-Tsinghua Center for Life Sciences, Academy for Advanced Interdisciplinary Studies, Peking University, Beijing 100871, China.

Systematic biology
|May 31, 2023
PubMed
概括
此摘要是机器生成的。

ERICA是一种新的深度学习方法,可以准确地推断进化关系,并从基因组数据中识别进化区域. 这种方法克服了计算方面的挑战,有助于研究各种生物体中的杂交和内进.

关键词:
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.进化关系的演化关系.混合化 混合化 混合化这是一个内进的进攻.

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科学领域:

  • 进化生物学是进化的生物学.
  • 基因组学就是基因组学.
  • 生物信息学是一种生物信息学.

背景情况:

  • 遗传学关系的解决是复杂的,因为许多物种的遗传混合.
  • 现有的全基因组解开进化历史的方法通常是计算密集的,需要特定的数据格式或大样本大小.

研究的目的:

  • 开发一种基于深度学习的新方法,以推断全基因组的进化关系,并识别局部内进区域.
  • 为分析复杂的基因组数据提供高效和有效的工具,克服当前方法的局限性.

主要方法:

  • 开发了ERICA (Evolutionary Relationship Inference with Convolutional Autoencoders),这是一个接受序列对齐的深度学习模型.
  • 埃里卡识别了跨基因组的不一致的家谱和内进模式.
  • 通过使用Heliconius蝶和全基因组组合的种群基因组数据验证ERICA.

主要成果:

  • 埃里卡有效地推断了全基因组的进化关系,并精确指出了进化的基因组段.
  • 该方法成功地确定了Heliconius蝶的杂交和内进模式.
  • 对大米的应用揭示了内侵入在化和适应中的重要作用.

结论:

  • 埃里卡提供了一种有效的基于深度学习的解决方案,用于分析整个基因组数据以解决进化关系.
  • 该工具有助于研究杂交和内进,这是进化和适应过程中的关键过程.
  • 埃里卡在米中的应用凸显了其在了解作物进化中的有用性.