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相关概念视频

Overview Of Cell Separation And Isolation01:20

Overview Of Cell Separation And Isolation

5.6K
Cell separation was first achieved in 1964 by S. H. Seal, who separated large tumor cells from the smaller blood cells using filtration. Two years later, Pohl and Hawk performed experiments on how cells respond differently to a nonuniform electric field based on the cell type. Such observations were the inception of cell separation methods, which allow isolating a single cell type from a heterogeneous sample.
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Cell Lines01:16

Cell Lines

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A cell line is a population of cells grown in vitro that can be subcultured over several generations. Normal cells cease to divide after a certain number of cell divisions, a process known as replicative senescence. This number, called the Hayflick limit, was conceptualized by Leonard Hayflick in 1961 when he observed that fetal cells grown in culture could only divide 40-60 times. This limit is due to the shortening of the telomeres during each round of cell division, preventing cell division...
7.3K
Genome Annotation and Assembly03:36

Genome Annotation and Assembly

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The genome refers to all of the genetic material in an organism. It can range from a few million base pairs in microbial cells to several billion base pairs in many eukaryotic organisms. Genome assembly refers to the process of taking the DNA sequencing data and putting it all back together in a correct order to create a close representation of the original genome. This is followed by the identification of functional elements on the newly assembled genome, a process called genome annotation.
18.8K
Heterochromatin02:38

Heterochromatin

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The extent of chromatin compaction can be studied by staining chromatin using specific DNA binding dyes. Under the microscope, the dense-compacted regions that take up more dye are called heterochromatin. Heterochromatin is further classified into two forms – constitutive heterochromatin and facultative heterochromatin.
Constitutive heterochromatin: It is a highly compact region of chromatin that is mostly concentrated in the centromere and telomere. Unlike euchromatin, the amino acid at...
11.4K
Cellular Differentiation00:57

Cellular Differentiation

2.6K
How does a complex organism such as a human develop from a single cell? It all starts from a single fertilized egg which gives rise to a vast array of cell types, such as nerve cells, muscle cells, and epithelial cells that characterize the adult? Throughout development and adulthood, cellular differentiation leads cells to assume their final morphology and physiology. Differentiation is the process by which unspecialized cells become specialized to carry out distinct functions.
A zygote is a...
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Determination01:51

Determination

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During embryogenesis, cells become progressively committed to different fates through a two-step process: specification followed by determination. Specification is demonstrated by removing a segment of an early embryo, “neutrally” culturing the tissue in vitro—for example, in a petri dish with simple medium—and then observing the derivatives. If the cultured region gives rise to cell types that it would normally generate in the embryo, this means that it is specified. In...
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相关实验视频

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Single-cell RNA-Seq of Defined Subsets of Retinal Ganglion Cells
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基于知识的诱导偏差和域调整用于细胞类型注释.

Zhenchao Tang1,2, Guanxing Chen1,2, Shouzhi Chen1,2

  • 1Artificial Intelligence Medical Research Center, School of Intelligent Systems Engineering, Shenzhen Campus of Sun Yat-sen University, Shenzhen, China.

Communications biology
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概括

一种新的方法KIDA通过将模型的诱导偏差与生物模式对齐来改进跨批细胞类型的注释. 这提高了细胞数据分析的概括性和准确性.

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科学领域:

  • 计算生物学 计算生物学
  • 生物信息学是一种生物信息学.
  • 机器学习 机器学习

背景情况:

  • 蜂数据分析面临的挑战是跨测量批次的域间隙.
  • 细胞注释方法的有效性取决于它们的诱导偏差和生物相关性.

研究的目的:

  • 通过整合生物知识,开发一种增强跨批细胞类型注释的方法.
  • 提高细胞注释模型的概括性和解释性.

主要方法:

  • 拟议的KIDA (基于知识的诱导偏差和域调整) 方法.
  • 纳入基于知识的诱导偏差,以使模型预测与生物功能模式保持一致.
  • 在多批查询数据集中使用伪标签和自我知识蒸来进行域调整.

主要成果:

  • 在基准实验中,KIDA证明了准确的跨批细胞类型注释.
  • 基于知识的诱导偏差改善了对未见批次的模型概括.
  • 域调整有效地减少了查询数据集中的分布差距.

结论:

  • 基达提供了一种强大的方法,可以在各种数据批次中准确地注释细胞类型.
  • 将诱导偏见与生物知识对齐,对于有效的计算生物学工具至关重要.
  • 该方法在推进单细胞数据分析和解释方面表现有前途.