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相关概念视频

Circadian Rhythms and Gene Regulation02:19

Circadian Rhythms and Gene Regulation

4.1K
The biological clock is involved in many aspects of regulating complex physiology in all animals. It was in 1935 when German zoologists, Hans Kalmus and Erwin Bünning, discovered the existence of circadian rhythm in Drosophila melanogaster. However, the internal molecular mechanisms behind the circadian clock remained a mystery until 1984, when Jeffrey C. Hall, Michael Rosbash, and Michael W. Young discovered the expression of the Per gene oscillating over a 24-hour cycle. In subsequent...
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Epigenetic Regulation01:37

Epigenetic Regulation

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Epigenetic changes alter the physical structure of the DNA without changing the genetic sequence and often regulate whether genes are turned on or off. This regulation ensures that each cell produces only proteins necessary for its function. For example, proteins that promote bone growth are not produced in muscle cells. Epigenetic mechanisms play an essential role in healthy development. Conversely, precisely regulated epigenetic mechanisms are disrupted in diseases like cancer.
X-chromosome...
3.0K
Inheritance of Chromatin Structures03:17

Inheritance of Chromatin Structures

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Epigenetics is the study of inherited changes in a cell's phenotype without changing the DNA sequences. It provides a form of memory for the differential gene expression pattern to maintain cell lineage, position-effect variegation, dosage compensation, and maintenance of chromatin structures such as telomeres and centromeres. For example, the structure and location of the centromere on chromosomes are epigenetically inherited. Its functionality is not dictated or ensured by the underlying...
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Histone Modification02:32

Histone Modification

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The histone proteins have a flexible N-terminal tail extending out from the nucleosome. These histone tails are often subjected to post-translational modifications such as acetylation, methylation, phosphorylation, and ubiquitination. Particular combinations of these modifications form “histone codes” that influence the chromatin folding and tissue-specific gene expression.
Acetylation
The enzyme histone acetyltransferase adds acetyl group to the histones. Another enzyme, histone...
13.3K
Position-effect Variegation02:32

Position-effect Variegation

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In 1928, a German botanist Emil Heitz observed the moss nuclei with a DNA binding dye. He observed that while some chromatin regions decondense and spread out in the interphase nucleus, others do not. He termed them euchromatin and heterochromatin, respectively. He proposed that the heterochromatin regions reflect a functionally inactive state of the genome. It was later confirmed that heterochromatin is transcriptionally repressed, and euchromatin is transcriptionally active chromatin.
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相关实验视频

Updated: Jul 5, 2025

Monitoring Cell-autonomous Circadian Clock Rhythms of Gene Expression Using Luciferase Bioluminescence Reporters
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Monitoring Cell-autonomous Circadian Clock Rhythms of Gene Expression Using Luciferase Bioluminescence Reporters

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有生物学依据的深度学习可以解释表观遗传时钟.

Aurel Prosz1, Orsolya Pipek2, Judit Börcsök1,3

  • 1Danish Cancer Institute, Copenhagen, Denmark.

Scientific reports
|January 15, 2024
PubMed
概括
此摘要是机器生成的。

我们开发了XAI-AGE,这是一种可解释的AI模型,可以使用DNA甲基化准确预测生物年龄. 这种模型不仅可以预测年龄,还可以揭示导致衰老的关键生物过程.

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Single-cell Resolution Fluorescence Live Imaging of Drosophila Circadian Clocks in Larval Brain Culture
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Single-cell Resolution Fluorescence Live Imaging of Drosophila Circadian Clocks in Larval Brain Culture

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相关实验视频

Last Updated: Jul 5, 2025

Monitoring Cell-autonomous Circadian Clock Rhythms of Gene Expression Using Luciferase Bioluminescence Reporters
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Epigenetic Regulation of Cardiac Differentiation of Embryonic Stem Cells and Tissues
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科学领域:

  • 老年学是指老年学的学科.
  • 生物信息学是一种生物信息学.
  • 人工智能的人工智能

背景情况:

  • 衰老与累积损伤和慢性疾病有关.
  • 像DNA甲基化这样的表观遗传机制在衰老中起作用.
  • 目前的表观遗传钟可以预测生物年龄,但缺乏解释性.

研究的目的:

  • 开发一种新的,可解释的深度神经网络模型 (XAI-AGE),用于准确的生物年龄预测.
  • 提高对控制衰老的生物过程的理解.
  • 从年龄预测模型中提供生物学上有意义的见解.

主要方法:

  • 开发了XAI-AGE,一个生物知情,可解释的深度神经网络.
  • 将模型应用于多种组织类型,以预测年龄.
  • 将XAI-AGE性能与现有的年龄预测模型进行比较.

主要成果:

  • XAI-AGE可以准确地预测各种组织的生物年龄.
  • 该模型的表现优于第一代表观遗传预测器.
  • XAI-AGE的性能与当前的深度学习模型相提并论.
  • 该模型允许推断对衰老过程的生物学上有意义的见解.

结论:

  • XAI-AGE为准确和可解释的生物年龄预测提供了一个强大的工具.
  • 该模型提升了我们对衰老背后的分子机制的理解.
  • 可解释的人工智能对未来的老年学研究具有重大潜力.