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相关概念视频

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

Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving

56
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...
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Mechanistic Models: Compartment Models in Individual and Population Analysis01:23

Mechanistic Models: Compartment Models in Individual and Population Analysis

43
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...
43
Model Approaches for Pharmacokinetic Data: Distributed Parameter Models01:06

Model Approaches for Pharmacokinetic Data: Distributed Parameter Models

70
Pharmacokinetic models are mathematical constructs that represent and predict the time course of drug concentrations in the body, providing meaningful pharmacokinetic parameters. These models are categorized into compartment, physiological, and distributed parameter models.
The distributed parameter models are specifically designed to account for variations and differences in some drug classes. This model is particularly useful for assessing regional concentrations of anticancer or...
70
Multicompartment Models: Overview01:14

Multicompartment Models: Overview

145
Multicompartment models are mathematical constructs that depict how drugs are distributed and eliminated within the body. They segment the body into several compartments, symbolizing various physiological or anatomical areas connected through drug transfer processes such as absorption, metabolism, distribution, and elimination.
These models offer a more comprehensive representation of drug behavior in the body than one-compartment models. They accommodate the complexity of drug distribution,...
145
State Space Representation01:27

State Space Representation

209
The frequency-domain technique, commonly used in analyzing and designing feedback control systems, is effective for linear, time-invariant systems. However, it falls short when dealing with nonlinear, time-varying, and multiple-input multiple-output systems. The time-domain or state-space approach addresses these limitations by utilizing state variables to construct simultaneous, first-order differential equations, known as state equations, for an nth-order system.
Consider an RLC circuit, a...
209
Clearance Models: Noncompartmental Models01:17

Clearance Models: Noncompartmental Models

61
Clearance is a pharmacokinetic parameter traditionally defined by compartment models, signifying the rate at which a drug is expelled from the body. However, a noncompartmental model offers an alternative method for assessing clearance, primarily employing empirical data obtained after administering a single drug dose.
The noncompartmental approach capitalizes on extensive sampling data, correlating the volume of distribution to systemic exposure and the administered dosage. This method enables...
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马尔科夫状态模型:优化或不优化

Robert E Arbon1,2, Yanchen Zhu1, Antonia S J S Mey1

  • 1EaStCHEM School of Chemistry, David Brewster Road, Joseph Black Building, The King's Buildings, Edinburgh EH9 3FJ, United Kingdom.

Journal of chemical theory and computation
|January 2, 2024
PubMed
概括
此摘要是机器生成的。

马尔科夫状态模型 (MSM) 的自动选择具有挑战性,因为超参数选择改变了优化目标的物理解释. 变量得分应该指导,而不是决定MSM超参数选择.

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科学领域:

  • 计算生物学是一种计算生物学.
  • 统计建模 统计建模
  • 机器学习是机器学习.

背景情况:

  • 马尔科夫状态模型 (MSM) 对于分析蛋白质结构动态和折叠至关重要.
  • 在MSM中超参数选择通常依赖于专家判断或像VAMP-2这样的变化得分.
  • 自动化超参数选择方法在机器学习中越来越常见.

研究的目的:

  • 对MSM进行自动化超参数选择的可行性进行调查.
  • 分析不同超参数对MSM模型选择和解释的影响.
  • 评估可靠的可变分数,以指导自动化MSM建设.

主要方法:

  • 估计和分析超过28万个马尔科夫状态模型.
  • 系统评估超参数选择及其对模型选择的影响.
  • 在不同的超参数设置下评估变化得分,包括VAMP-2.

主要成果:

  • 超参数差异可以显著改变对优化目标的物理解释,使自动选择复杂化.
  • 在VAMP得分中强制执行平衡条件可能会导致不一致的模型选择.
  • 滞后时间和得分放松过程的数量对基于VAMP-2的模型选择的影响很小.

结论:

  • 仅使用变量分数的MSM的自动选择是困难的,因为解释的转移.
  • 变量得分和模型可观测值应作为指南,而不是确定标准,用于超参数选择.
  • 对MSM属性的全面调查对于强大的超参数选择至关重要.