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相关概念视频

What is Gene Expression?01:42

What is Gene Expression?

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Overview
Gene expression is the process in which DNA directs the synthesis of functional products, that is, proteins. Cells can regulate gene expression at various stages. It allows organisms to generate different cell types and enables cells to adapt to internal and external factors.
Genetic Information Flows from DNA to RNA to Protein
A gene is a stretch of DNA that serves as the blueprint for functional RNAs and proteins. Since DNA is made up of nucleotides and proteins consist of amino...
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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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Mechanistic models play a crucial role in algorithms for numerical problem-solving, particularly in nonlinear mixed effects modeling (NMEM). These models aim to minimize specific objective functions by evaluating various parameter estimates, leading to the development of systematic algorithms. In some cases, linearization techniques approximate the model using linear equations.
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Combinatorial gene control is the synergistic action of several transcriptional factors to regulate the expression of a single gene. The absence of one or more of these factors may lead to a significant difference in the level of gene expression or repression.
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使用机器学习方法描述基因表达数据的模型的识别

Lucas F Jansen Klomp1,2, Elena Queirolo3, Janine N Post2

  • 1Mathematics of Imaging & AI, Department of Applied Mathematics, University of Twente, Enschede, The Netherlands.

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本研究介绍了使用神经网络的框架,以识别基因调节网络的机制普通微分方程模型. 这种方法提高了模型的可解释性,并为细胞分化等细胞过程产生了新的假设.

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科学领域:

  • 系统生物学
  • 计算生物学
  • 机器学习

背景情况:

  • 机械常规微分方程 (ODE) 模型对于理解细胞过程和制定生物假设至关重要.
  • 这些模型的数据驱动推断正在增加,但在不失去可解释性的情况下整合机器学习 (ML) 仍然是一个挑战.
  • 基因调节网络 (GRNs) 控制复杂的细胞内动力学,包括细胞分化.

研究的目的:

  • 提供一个利用神经网络的框架,用于识别可解释的,数据驱动的ODE模型.
  • 利用ML在GRN中提出新的连接,提高模型准确性和生物洞察力.
  • 对细胞内过程的动态产生可测试的假设.

主要方法:

  • 开发一个整合神经网络与机械 ODE 建模的框架.
  • 应用图形自编码模型来推断和建议GRN中的连接.
  • 对依赖时间的细胞内过程,如细胞分化方法的验证.

主要成果:

  • 证明了图形自编码器的成功应用,以建议新的GRN连接.
  • 展示了改进的图形结构如何提高动态系统的识别.
  • 对已识别的细胞过程的动态产生了新的假设.

结论:

  • 拟议的框架有效地使用神经网络来识别可解释的GRN机制模型.
  • 这种方法为复杂的生物系统提供了新的,数据驱动的假设.
  • 机器学习的整合为推进系统生物学和了解细胞机制提供了强大的工具.