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Mechanistic Models: Compartment Models in Individual and Population Analysis01:23

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Mechanistic models are utilized in individual analysis using single-source data, but imperfections arise due to data collection errors, preventing perfect prediction of observed data. The mathematical equation involves known values (Xi), observed concentrations (Ci), measurement errors (εi), model parameters (ϕj), and the related function (ƒi) for i number of values. Different least-squares metrics quantify differences between predicted and observed values. The ordinary least...
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分享-主题:单细胞多原子数据的贝叶斯可解释建模.

Nour El Kazwini1, Guido Sanguinetti2

  • 1Theoretical and Scientific Data Science, Scuola Internazionale Superiore di Studi Avanzati, Trieste, Italy.

Genome biology
|February 23, 2024
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概括
此摘要是机器生成的。

SHARE-Topic是一种新的贝叶斯模型,分析复杂的单细胞多原子数据. 它揭示了基因调节模式,并将基因与调节器联系起来,克服了生物见解的数据噪声和稀疏性.

关键词:
贝叶斯模型是贝叶斯模型.基因调节 基因调节癌症中的基因调节器可以解释性 解释性淋巴瘤是一种淋巴瘤.单细胞多组体的单细胞多组体.

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科学领域:

  • 基因组学就是基因组学.
  • 表观遗传学 在表观遗传学中,表观遗传学是指表观遗传学.
  • 计算生物学 计算生物学

背景情况:

  • 多原子单细胞技术为基因调节提供了深刻的见解.
  • 噪音和稀疏的数据给分析带来了重大的统计挑战.
  • 提取生物知识需要先进的计算方法.

研究的目的:

  • 开发一个贝叶斯生成模型,SHARE-Topic,用于分析多原子单细胞数据.
  • 为应对杂和稀疏的多原子数据集所带来的统计挑战.
  • 为了识别跨欧米层的共同变异模式,并解释数据复杂性.

主要方法:

  • 在SHARE-Topic框架内利用贝叶斯推理和主题建模.
  • 将模型应用于来自不同技术平台的单细胞数据.
  • 开发了用于低维数据表示和关联分析的方法.

主要成果:

  • SHARE-Topic成功地确定了不同欧米层之间的共同变异的共同模式.
  • 该模型生成了低维的表示,重复了已知的生物信息.
  • 在单个细胞内建立了基因和远端调节元件之间的关联.

结论:

  • SHARE-Topic为理解复杂的多原子单细胞数据提供了一个可解释的框架.
  • 该模型有效地解决了数据噪声和稀疏性,从而实现了强大的生物发现.
  • 促进研究表观遗传机制和单细胞水平的基因调节.