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相关概念视频

Causality in Epidemiology01:21

Causality in Epidemiology

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Causality or causation is a fundamental concept in epidemiology, vital for understanding the relationships between various factors and health outcomes. Despite its importance, there's no single, universally accepted definition of causality within the discipline. Drawing from a systematic review, causality in epidemiology encompasses several definitions, including production, necessary and sufficient, sufficient-component, counterfactual, and probabilistic models. Each has its strengths and...
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Updated: Sep 10, 2025

Spatial Profiling of Protein and RNA Expression in Tissue: An Approach to Fine-Tune Virtual Microdissection
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从使用CASCAT的空间转录组学推断因果轨迹.

Yingying Yu1,2, Wan Nie1, Qianqian Zhang1,3

  • 1Department of Computer Science, City University of Hong Kong, 83 Tat Chee Ave, Kowloon Tong, 999077, Hong Kong.

Nucleic acids research
|August 19, 2025
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概括
此摘要是机器生成的。

我们开发了CASCAT,这是一个新的因果模型,用于从空间转录组学数据中推断细胞分化轨迹. CASCAT准确地模拟细胞状态动态,并预测药物反应,推进计算生物学和药物发现.

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科学领域:

  • 计算生物学是一种计算生物学.
  • 基因组学就是基因组学.
  • 系统生物学 系统生物学

背景情况:

  • 空间轨迹推断对于理解细胞分化和组织动态至关重要.
  • 当前的方法往往依赖于空间接近,忽视了细胞状态转换的马科维特征.
  • 由于马尔科夫等价性,从高维,非线性数据推断独特的轨迹存在挑战.

研究的目的:

  • 介绍CASCAT,一个树状结构因果模型,集成马科维特征,用于独特的细胞分化轨迹推断.
  • 解决处理复杂生物数据的现有方法的局限性.

主要方法:

  • 开发了CASCAT,一种新的树状结构因果模型.
  • 将马科维特征整合到因果推理框架中.
  • 应用CASCAT分析模拟和真实单细胞RNA测序 (scRNA-seq) 和空间转录组学数据集.

主要成果:

  • CASCAT的性能优于六种最先进的scRNA-seq方法和三种领先的空间轨迹推断方法.
  • 准确地确定了细胞成熟轨迹,并揭示了小鼠内嗅球中的Wnt信号通路.
  • 与RNA速度方法相比,在口腔状细胞癌中,药物反应预测精度提高了6.8%.

结论:

  • CASCAT为推断独特的细胞分化轨迹提供了一个强大的框架.
  • 在准确性和生物洞察力方面,与现有方法相比,显著改进.
  • 提供了推进计算机辅助药物发现和理解复杂生物系统的潜力.