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相关概念视频

Model Approaches for Pharmacokinetic Data: Distributed Parameter Models01:06

Model Approaches for Pharmacokinetic Data: Distributed Parameter Models

68
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...
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Survival Tree01:19

Survival Tree

80
Survival trees are a non-parametric method used in survival analysis to model the relationship between a set of covariates and the time until an event of interest occurs, often referred to as the "time-to-event" or "survival time." This method is particularly useful when dealing with censored data, where the event has not occurred for some individuals by the end of the study period, or when the exact time of the event is unknown.
 Building a Survival Tree
Constructing a...
80
Model Approaches for Pharmacokinetic Data: Compartment Models01:14

Model Approaches for Pharmacokinetic Data: Compartment Models

92
Compartmental analysis is a widely adopted approach to characterizing drug pharmacokinetics. It uses compartment models that conceptualize the body as a collection of reversibly communicating compartments, each representing a group of tissues exhibiting similar drug distribution characteristics. The movement rate of the drug between these compartments is typically described by first-order kinetics.
Two primary types of compartment models are recognized: mammillary and catenary. The more...
92
Analysis Methods of Pharmacokinetic Data: Model and Model-Independent Approaches01:14

Analysis Methods of Pharmacokinetic Data: Model and Model-Independent Approaches

117
Drug disposition in the body is a complex process and can be studied using two major approaches: the model and the model-independent approaches.
The model approach uses mathematical models to describe changes in drug concentration over time. Pharmacokinetic models help characterize drug behavior in patients, predict drug concentration in the body fluids, calculate optimum dosage regimens, and evaluate the risk of toxicity. However, ensuring that the model fits the experimental data accurately...
117
Statistical Analysis: Overview01:11

Statistical Analysis: Overview

6.6K
When we take repeated measurements on the same or replicated samples, we will observe inconsistencies in the magnitude. These inconsistencies are called errors. To categorize and characterize these results and their errors, the researcher can use statistical analysis to determine the quality of the measurements and/or suitability of the methods.
One of the most commonly used statistical quantifiers is the mean, which is the ratio between the sum of the numerical values of all results and the...
6.6K
Time-Series Graph00:54

Time-Series Graph

4.4K
A time-series graph is a line graph with repeated measurements taken at successive intervals of time. It is also called a time series chart. To construct a time-series graph, one must look at both pieces of a paired data set. The horizontal axis is used to plot the time increments, and the vertical axis is used to plot the values of the variable that one is measuring. By using the axes in this way, each point on the graph will correspond to time and a measured quantity. The points on the graph...
4.4K

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A Method of Trigonometric Modelling of Seasonal Variation Demonstrated with Multiple Sclerosis Relapse Data
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时间序列数据的贝叶斯建模 (BayModTS) - - 一个公平的工作流程来处理稀疏和高度可变的数据.

Sebastian Höpfl1, Mohamed Albadry2,3, Uta Dahmen2

  • 1Institute for Stochastics and Applications, University of Stuttgart, 70569 Stuttgart, Germany.

Bioinformatics (Oxford, England)
|May 14, 2024
PubMed
概括

我们开发了BayModTS,这是一个贝叶斯模型工作流程,用于稀疏和可变时间序列数据. 这种方法始终处理不确定性,使生物系统的强有力的分析和特定条件的动态的识别.

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科学领域:

  • 系统生物学 系统生物学
  • 计算生物学是一种计算生物学.
  • 数据科学是数据科学.

背景情况:

  • 系统生物学中的定量动态建模面临着高可变性,低分辨率时间序列数据的挑战.
  • 整合这些数据,同时始终处理不确定性对于准确的生物系统理解至关重要.

研究的目的:

  • 介绍BayModTS (时间序列数据的贝叶斯建模),用于分析稀疏和可变时间序列的FAIR工作流.
  • 证明工作流在将数据不确定性转移到模型预测和识别特定条件动态方面的能力.

主要方法:

  • 开发了BayModTS,这是一个贝叶斯模型工作流程,用于处理和分析时间序列数据.
  • 从数据转移到模型预测实现了一致的不确定性转移.
  • 利用参数化模型来结合过程知识.

主要成果:

  • BayModTS成功地处理了三个不同的肝脏数据集 (动物MRI,小鼠药理动力学,人类CT) 的稀疏和可变时间序列数据.
  • 工作流程有效地转移了不确定性,并确定了条件之间的动态的可信差异.
  • 在分析生物时间序列数据方面表现出强度和多功能性.

结论:

  • BayModTS为分析具有挑战性的生物时间序列数据提供了强大的解决方案.
  • 工作流程通过一致处理不确定性,提高了定量动态建模的可靠性.
  • 通过改进数据集成和分析,促进对生物系统的更深入的理解.