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相关概念视频

RNA-seq03:21

RNA-seq

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RNA sequencing, or RNA-Seq, is a high-throughput sequencing technology used to study the transcriptome of a cell. Transcriptomics helps to interpret the functional elements of a genome and identify the molecular constituents of an organism. Additionally, it also helps in understanding the development of an organism and the occurrence of diseases. 
Before the discovery of RNA-seq, microarray-based methods and Sanger sequencing were used for transcriptome analysis. However, while...
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相关实验视频

Updated: May 3, 2026

Photodiode-Based Optical Imaging for Recording Network Dynamics with Single-Neuron Resolution in Non-Transgenic Invertebrates
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一个基于物理学的神经SDE网络,用于从时间序列scRNA-seq数据中学习细胞动态.

Qi Jiang1,2, Lin Wan1,2

  • 1Academy of Mathematics and Systems Science, Chinese Academy of Sciences, Beijing 100190, China.

Bioinformatics (Oxford, England)
|September 4, 2024
PubMed
概括
此摘要是机器生成的。

我们介绍PI-SDE,一个基于物理的框架,用于从单细胞RNA测序数据中重建细胞潜在能量景观. 这种方法提高了预测准确度和细胞分化动态的生物解释性.

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科学领域:

  • 计算生物学 计算生物学
  • 系统生物学 系统生物学
  • 单细胞基因组学 单细胞基因组学

背景情况:

  • 从时间序列单细胞RNA测序 (scRNA-seq) 数据中重建细胞潜在能量景观 (瓦丁顿景观) 对于理解细胞动态至关重要.
  • 目前的数据驱动方法往往缺乏物理原理,限制了预测的准确性和可解释性.

研究的目的:

  • 开发一个基于物理学的框架,PI-SDE,用于准确和可解释的细胞动态重建.
  • 将物理原理,特别是汉密尔顿-雅各比方程整合到神经随机微分方程中,用于从scRNA-seq数据中学习.

主要方法:

  • 提出PI-SDE,一个基于物理学的神经随机微分方程 (SDE) 框架.
  • 结合汉密尔顿 - 雅各比 (HJ) 方程与神经SDE,强制执行HJ方程以根据潜在能量理论重建细胞潜在能量函数.
  • 将最少行动原则纳入学习过程.

主要成果:

  • 在实时scRNA-seq数据集上,PI-SDE证明了基因表达在持久时间点上的预测准确度有所提高.
  • 重建的潜在能量景观为细胞分化提供了生物学上可解释的见解.
  • 该框架显示了增强的模型性能,稳定性和稳定性.

结论:

  • 在scRNA-seq数据分析中,纳入HJ规范化术语对于准确的动态推理和预测至关重要.
  • PI-SDE提供了一个强大的和可解释的方法来建模细胞分化动态.
  • 该PI-SDE软件是公开可用的,用于更广泛的研究应用.