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相关概念视频

Uncertainty: Overview00:59

Uncertainty: Overview

495
In analytical chemistry, we often perform repetitive measurements to detect and minimize inaccuracies caused by both determinate and indeterminate errors. Despite the cares we take, the presence of random errors means that repeated measurements almost never have exactly the same magnitude. The collective difference between these measurements - observed values - and the estimated or expected value is called uncertainty. Uncertainty is conventionally written after the estimated or expected value.
495
Propagation of Uncertainty from Random Error00:59

Propagation of Uncertainty from Random Error

620
An experiment often consists of more than a single step. In this case, measurements at each step give rise to uncertainty. Because the measurements occur in successive steps, the uncertainty in one step necessarily contributes to that in the subsequent step. As we perform statistical analysis on these types of experiments, we must learn to account for the propagation of uncertainty from one step to the next. The propagation of uncertainty depends on the type of arithmetic operation performed on...
620
Propagation of Uncertainty from Systematic Error01:10

Propagation of Uncertainty from Systematic Error

450
The atomic mass of an element varies due to the relative ratio of its isotopes. A sample's relative proportion of oxygen isotopes influences its average atomic mass. For instance, if we were to measure the atomic mass of oxygen from a sample, the mass would be a weighted average of the isotopic masses of oxygen in that sample. Since a single sample is not likely to perfectly reflect the true atomic mass of oxygen for all the molecules of oxygen on Earth, the mass we obtain from this...
450
Mechanistic Models: Compartment Models in Individual and Population Analysis01:23

Mechanistic Models: Compartment Models in Individual and Population Analysis

23
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...
23
Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving01:29

Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving

38
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.
In individual population analyses, different algorithms are employed, such as Cauchy's method, which uses a...
38

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相关实验视频

Updated: May 23, 2025

Author Spotlight: Evaluation of Protein-Condensate Dynamics in Live Human Cells
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Author Spotlight: Evaluation of Protein-Condensate Dynamics in Live Human Cells

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在动态生物系统中进行不确定性量化的一致预测.

Alberto Portela1, Julio R Banga1, Marcos Matabuena2

  • 1Computational Biology Lab, MBG-CSIC (Spanish National Research Council), Pontevedra, Galicia, Spain.

PLoS computational biology
|May 12, 2025
PubMed
概括

本研究引入了用于动态系统生物学模型中不确定性定量化的新型符合性预测算法. 这些方法为贝叶斯方法提供了强大的,可扩展的替代方案,提高了对模型预测的信心.

科学领域:

  • 系统生物学 系统生物学
  • 计算生物学 计算生物学
  • 统计建模 统计建模

背景情况:

  • 不确定性量化 (UQ) 对于动态系统生物学模型至关重要,因为它具有非线性和参数灵敏度.
  • 当前的UQ方法,通常是贝叶斯式的,需要先前的分布,并且可以是计算密集的.
  • 贝叶斯的UQ中的参数假设可能并不总是与生物复杂性保持一致.

研究的目的:

  • 提出符合性预测方法作为动态生物系统中UQ的替代方案.
  • 为系统生物学应用而设计的两种新型符合性算法.
  • 为了证明这些新的UQ方法的稳定性和可扩展性.

主要方法:

  • 对动态生物模型应用符合性预测原理.
  • 开发两种用于非对称不确定性量化的新算法.
  • 通过系统生物学中的说明性场景进行验证.

主要成果:

  • 合规算法为UQ提供了非对称的保证.
  • 这些方法提高了稳定性和可扩展性,即使是错误的模型.
  • 作为贝叶斯式UQ的补充或替代品的有效性已被证明.

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相关实验视频

Last Updated: May 23, 2025

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结论:

  • 符合性预测为系统生物学中的UQ提供了一个强大的框架.
  • 拟议的算法提供可靠和高效的不确定性估计.
  • 这些方法提升了动态生物模型的预测能力.