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相关概念视频

EPS and iPS Cells in Disease Research01:21

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Embryonic and induced pluripotent stem cells are excellent models for disease research because of their ability to self-renew and differentiate into most cell types. Somatic cells from a patient are isolated and reprogrammed into induced pluripotent stem cells or iPSCs. These iPSCs are later differentiated into the desired cell type, which mirrors the diseased cell of the patient. In this way, disease models have been created for investigating diseases such as Down syndrome, type I diabetes,...
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Stem cells are undifferentiated cells that divide and produce different types of cells. Ordinarily, cells that have differentiated into a specific cell type are post-mitotic—that is, they no longer divide. However, scientists have found a way to reprogram these mature cells so that they “de-differentiate” and return to an unspecialized, proliferative state. These cells are also pluripotent like embryonic stem cells—able to produce all cell types—and are therefore...
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用机器学习方法对人类多能干细胞的表型识别进行形态信号处理.

Ekaterina Vedeneeva1, Vitaly Gursky2,3, Maria Samsonova1

  • 1Department of Physics and Mechanics & Mathematical Biology and Bioinformatics Laboratory, Peter the Great St. Petersburg Polytechnic University, 195251 Saint Petersburg, Russia.

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|November 25, 2023
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概括

选择人类多能干细胞克隆用于再生医学可以自动化. 机器学习模型使用结合的细胞和殖民地形态学准确预测细胞质量,改善医疗应用的质量控制.

关键词:
最好的克隆人类胚胎干细胞人类多能干细胞干细胞机器学习是机器学习.形态现象类型 形态现象类型

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科学领域:

  • 干细胞生物学 干细胞生物学
  • 再生医学是一种再生医学.
  • 生物信息学是一种生物信息学.

背景情况:

  • 人类多能干细胞 (hPSCs) 对于再生医学至关重要,因为它们具有自我更新和分化潜力.
  • 高质量的hPSC克隆的自动选择是必不可少的,但具有挑战性.
  • 需要使用无标签的,非侵入性的方法来评估hPSC殖民地表型.

研究的目的:

  • 开发和评估机器学习模型来分类hPSC殖民地表型 ("好"或"坏").
  • 为了确定最佳的形态参数和分类方法来预测hPSC质量.
  • 建立在医疗环境中自动化hPSC质量控制的基础.

主要方法:

  • 利用相对比成像从hPSC殖民地提取形态参数.
  • 应用机器学习算法,包括人工神经网络,物流回归和随机森林.
  • 使用细胞形态学,殖民地形态学和组合参数比较分类准确度.

主要成果:

  • 使用细胞形态的模型达到67%的准确性 (人工神经网络).
  • 使用殖民地形态的模型达到75%的准确性 (逻辑回归).
  • 将细胞和殖民地形态与随机森林结合起来,获得了99%的准确性.

结论:

  • 细胞和殖民地形态特征都是准确的hPSC表型预测所必需的.
  • 整合各种形态数据的机器学习模型显著增强了hPSC的自动化质量控制.
  • 这些发现支持在医疗应用中开发用于hPSC选择的自动化系统.