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相关概念视频

Improving Translational Accuracy02:07

Improving Translational Accuracy

Base complementarity between the three base pairs of mRNA codon and the tRNA anticodon is not a failsafe mechanism. Inaccuracies can range from a single mismatch to no correct base pairing at all. The free energy difference between the correct and nearly correct base pairs can be as small as 3 kcal/ mol. With complementarity being the only proofreading step, the estimated error frequency would be one wrong amino acid in every 100 amino acids incorporated. However, error frequencies observed in...
Cell Specific Gene Expression01:58

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Multicellular organisms contain a variety of structurally and functionally distinct cell types, but the DNA in all the cells originated from the same parent cells. The differences in the cells can be attributed to the differential gene expression. Liver cells, whose functions include detoxification of blood, production of bile to metabolize fats, and synthesis of proteins essential for metabolism, must express a specific set of genes to perform their functions. Gene expression also varies with...
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Sequencing of the human genome has opened up several best-kept secrets of the genome. Scientists have identified thousands of genome variations that exist within a population. These variations can be a single nucleotide or a larger chromosomal variation.
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Replicative cell senescence is a property of cells that allows them to divide a finite number of times throughout the organism's lifespan while preventing excessive proliferation. Replicative senescence is associated with the gradual loss of the telomere — short, repetitive DNA sequences found at the end of the chromosomes. Telomeres are bound by a group of proteins to form a protective cap on the ends of chromosomes. Embryonic stem cells express telomerase — an enzyme that adds the telomeric...
Improving Translational Accuracy02:07

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Base complementarity between the three base pairs of mRNA codon and the tRNA anticodon is not a failsafe mechanism. Inaccuracies can range from a single mismatch to no correct base pairing at all. The free energy difference between the correct and nearly correct base pairs can be as small as 3 kcal/ mol. With complementarity being the only proofreading step, the estimated error frequency would be one wrong amino acid in every 100 amino acids incorporated. However, error frequencies observed in...
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scNODE:时间单细胞转录基因数据预测的生成模型.

Jiaqi Zhang1, Erica Larschan2,3, Jeremy Bigness2

  • 1Department of Computer Science, Brown University, Providence, RI 02906, United States.

Bioinformatics (Oxford, England)
|September 4, 2024
PubMed
概括
此摘要是机器生成的。

scNODE是一种深度学习模型,可以预测细胞发育研究中未被观察到的时间点的基因表达. 这种计算方法增强了细胞轨迹推断和在基因扰动分析,使用单细胞RNA测序数据.

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科学领域:

  • 计算生物学 计算生物学
  • 基因组学就是基因组学.
  • 机器学习 机器学习

背景情况:

  • 单细胞基因表达分析对于理解细胞发育至关重要.
  • 目前的方法面临的局限性是由于在实验中稀疏,离散的时间点采样.
  • 缺少时间数据阻碍了对细胞发育轨迹的全面分析.

研究的目的:

  • 开发一个深度学习模型,scNODE,用于预测单细胞基因表达在未观察到的时间点.
  • 为应对单细胞RNA测序 (scRNA-seq) 数据中缺少时间信息的挑战.
  • 改进下游分析,如细胞轨迹推断和基因扰动研究.

主要方法:

  • scNODE集成了一个变量自编码器与神经普通微分方程.
  • 该模型利用连续和非线性潜空间进行基因表达预测.
  • 一个动态正规化术语被纳入了针对分布转移的稳定性.

主要成果:

  • 与最先进的方法相比,scNODE在三个现实世界scRNA-seq数据集上表现出优越的预测性能.
  • 来自scNODE的预测在缺失时间点的场景中改善了细胞轨迹推断.
  • 学习的隐性空间对于in silico扰动分析非常有价值.

结论:

  • scNODE有效地预测了在未观察到的时间点上的基因表达,填补了关键数据缺口.
  • 该模型通过实现持续的时间建模来增强细胞发育的分析.
  • scNODE为推进发育生物学和计算基因组学研究提供了一个强大的工具.