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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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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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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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Model Approaches for Pharmacokinetic Data: Distributed Parameter Models01:06

Model Approaches for Pharmacokinetic Data: Distributed Parameter Models

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Pharmacokinetic models are mathematical constructs that represent and predict the time course of drug concentrations in the body, providing meaningful pharmacokinetic parameters. These models are categorized into compartment, physiological, and distributed parameter models.
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One-Compartment Open Model: Wagner-Nelson and Loo Riegelman Method for ka Estimation01:24

One-Compartment Open Model: Wagner-Nelson and Loo Riegelman Method for ka Estimation

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This lesson introduces two critical methods in pharmacokinetics, the Wagner-Nelson and Loo-Riegelman methods, used for estimating the absorption rate constant (ka) for drugs administered via non-intravenous routes. The Wagner-Nelson method relates ka to the plasma concentration derived from the slope of a semilog percent unabsorbed time plot. However, it is limited to drugs with one-compartment kinetics and can be impacted by factors like gastrointestinal motility or enzymatic degradation.
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相关实验视频

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Author Spotlight: AI-Driven Trypanosome Species Detection from Microscopic Images
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ModelRevelator:通过深度学习来快速估计家族遗传模型.

Sebastian Burgstaller-Muehlbacher1, Stephen M Crotty2, Heiko A Schmidt1

  • 1Center for Integrative Bioinformatics Vienna, Max Perutz Labs, University of Vienna and Medical University of Vienna, Vienna BioCenter (VBC) 5, 1030 Vienna, Austria.

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概括
此摘要是机器生成的。

ModelRevelator使用神经网络来快速准确地选择生物遗传学中的进化模型. 这种机器学习方法避免了计算密集的树重建,大大节省了时间和资源.

关键词:
人工智能的人工智能是人工智能.深度学习是一种深度学习.遗传学模型估计的结果人类遗传学 是一个学科.人类基因组学是什么?

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科学领域:

  • 计算生物学 计算生物学
  • 人类遗传学 是一个学科.
  • 机器学习 机器学习

背景情况:

  • 选择适当的进化模型对于准确的遗传树重建至关重要.
  • 由于树重建和参数优化,像最大概率 (ML) 这样的传统方法在计算上昂贵.
  • 现有的模型选择工具对于大型数据集可能并不总是从计算上可行的.

研究的目的:

  • 介绍ModelRevelator,一种使用深度神经网络选择进化模型的新工具.
  • 为了证明神经网络可以在不重建树或计算概率的情况下进行模型选择.
  • 为传统的模型选择方法提供一个计算效率高的替代方案.

主要方法:

  • 开发两个深度神经网络:NNmodelfind用于模型推和NNalphafind用于速率异质性 (Γ-分布) 和其形状参数 (ɑ).
  • 在ModelRevelator中输入多个序列对齐 (MSA),以快速输出模型和参数.
  • 与基于概率的方法和机器学习工具ModelTeller进行比较分析.

主要成果:

  • ModelRevelator成功地推了进化模型,包括速率异质性和 ɑ 参数.
  • 在各种参数设置中,性能与已建立的基于概率的方法和ModelTeller可比.
  • 该工具在培训和未见经验数据上都表现出强的性能.

结论:

  • 模型揭示器提供了一个计算效率高,准确的方法,用于进化模型的选择在植物遗传学.
  • 对于那些在计算上无法使用的传统方法而面临的系谱学家来说,它特别有价值.
  • 基于神经网络的方法在简化家族遗传学分析方面具有显著的潜力.