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相关概念视频

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

Mechanistic Models: Compartment Models in Individual and Population Analysis

36
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...
36
Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving01:29

Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving

48
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...
48
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...
68
Model Approaches for Pharmacokinetic Data: Compartment Models01:14

Model Approaches for Pharmacokinetic Data: Compartment Models

88
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...
88
Analysis Methods of Pharmacokinetic Data: Model and Model-Independent Approaches01:14

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

112
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...
112
One-Compartment Open Model: Wagner-Nelson and Loo Riegelman Method for ka Estimation01:24

One-Compartment Open Model: Wagner-Nelson and Loo Riegelman Method for ka Estimation

456
This lesson introduces two critical methods in pharmacokinetics, the Wagner-Nelson and Loo-Riegelman methods, used for estimating the absorption rate constant (ka) for drugs administered via non-intravenous routes. The Wagner-Nelson method relates ka to the plasma concentration derived from the slope of a semilog percent unabsorbed time plot. However, it is limited to drugs with one-compartment kinetics and can be impacted by factors like gastrointestinal motility or enzymatic degradation.
On...
456

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相关实验视频

Updated: Jun 22, 2025

Heuristic Mining of Hierarchical Genotypes and Accessory Genome Loci in Bacterial Populations
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用同型编码的基因型数据进行逻辑和线性回归的保护隐私的模型评估.

Seungwan Hong1, Yoolim A Choi1, Daniel S Joo2

  • 1Department of Biomedical Informatics, Columbia University, New York, NY 10032, USA; New York Genome Center, New York, NY 10013, USA.

Journal of biomedical informatics
|June 27, 2024
PubMed
概括

本研究介绍了一种安全的方法,用于评估使用同态加密的遗传预测模型. 它通过加密所有数据和模型参数来保护患者的隐私,确保分析过程中的保密性.

关键词:
基因型表型协会 基因型表型协会同型的加密是同型的.增强隐私的技术 增强隐私的技术

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Navigating MARRVEL, a Web-Based Tool that Integrates Human Genomics and Model Organism Genetics Information
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An R-Based Landscape Validation of a Competing Risk Model
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An R-Based Landscape Validation of a Competing Risk Model

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相关实验视频

Last Updated: Jun 22, 2025

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08:03

Heuristic Mining of Hierarchical Genotypes and Accessory Genome Loci in Bacterial Populations

Published on: December 7, 2021

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Navigating MARRVEL, a Web-Based Tool that Integrates Human Genomics and Model Organism Genetics Information
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科学领域:

  • 人口遗传学 人口遗传学
  • 密码学 密码学 密码学 密码学
  • 生物信息学是一种生物信息学.

背景情况:

  • 线性回归和逻辑回归对于在人口遗传学中分析大型遗传数据集至关重要.
  • 分析敏感的基因型和表型数据引发了严重的患者隐私问题.
  • 现有的同型加密方法用于安全计算并不能完全保护共享模型的机密性.

研究的目的:

  • 为线性回归和逻辑回归开发一种安全的模型评估方法,保证患者的保密性.
  • 解决以前加密方法在保护共享遗传模型方面的局限性.

主要方法:

  • 一种使用同态加密的新方法,用于在人口遗传学中安全的模型评估.
  • 输入基因型,输出表型和模型参数的加密,以保护隐私.
  • 适用于涉及遗传数据分析的六个预测任务.

主要成果:

  • 拟议的方法确保在模型推理过程中不会有私人信息泄露.
  • 在所有评估的预测任务中都实现了高精度 (≥93%).
  • 在大约200个基因组中,每个人推断时间小于10秒.

结论:

  • 证明了私人模型评估在人口遗传学中的线性和逻辑回归的可行性.
  • 证实了通过理论上的安全保证来保护患者保密的能力.
  • 为可重复性和进一步研究提供开源实现和测试数据.