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相关概念视频

Model Approaches for Pharmacokinetic Data: Physiological Models01:15

Model Approaches for Pharmacokinetic Data: Physiological Models

Physiological models in pharmacokinetics are instrumental in understanding the distribution and elimination of drugs within the body. These models describe the drug concentration within target organs, influenced by factors such as drug uptake, tissue volume, and blood flow. Drug uptake is governed by the partition coefficient, which signifies the drug concentration ratio in tissue to that in the blood. The blood flow rate to a specific tissue is expressed as Qt, and the rate of change in tissue...
Model Approaches for Pharmacokinetic Data: Distributed Parameter Models01:06

Model Approaches for Pharmacokinetic Data: Distributed Parameter Models

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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相关实验视频

Updated: Jun 21, 2026

Closed-loop Neuro-robotic Experiments to Test Computational Properties of Neuronal Networks
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预测和预测基于生物信息的神经网络的随机代理模型数据.

John T Nardini1

  • 1Department of Mathematics and Statistics, The College of New Jersey, Ewing, NJ, 08628, USA. nardinij@tcnj.edu.

Bulletin of mathematical biology
|September 22, 2024
PubMed
概括
此摘要是机器生成的。

生物信息神经网络 (BINNs) 创建可解释的微分方程 (DE) 模型,以准确预测基于代理的模型 (ABM) 集体迁移. 这种方法可以预测新的数据,并有效地探索未知参数空间.

关键词:
基于代理人的建模.数据驱动的建模.不同方程的微分方程.机器学习是机器学习.

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科学领域:

  • 计算生物学 计算生物学
  • 数学建模的数学建模
  • 人工智能的人工智能

背景情况:

  • 集体细胞迁移对于诸如伤口愈合和发育等生物过程至关重要.
  • 基于代理的模型 (ABM) 模拟集体迁移,但计算密集且难以参数化.
  • 平均场微分方程 (DE) 模型提供更快的模拟,但可以在某些参数空间中不准确地表示ABM行为.

研究的目的:

  • 开发一种使用生物信息神经网络 (BINNs) 来创建准确和可解释的集体迁移DE模型的方法.
  • 为了能够在未见的数据和未开发的参数区域中预测ABM行为.
  • 为了提高ABM的参数空间探索的效率.

主要方法:

  • 训练生物信息神经网络 (BINNs) 来从ABM数据中学习可解释的微分方程 (DE) 模型.
  • 使用BINN引导的部分微分方程 (PDE) 模拟来预测未来的ABM数据.
  • 将BINN引导的PDE模拟与多变量插入相结合,以在新的参数值下预测ABM行为.
  • 通过三种不同的集体迁移的ABM验证该方法.

主要成果:

  • 由BINN引导的PDE模拟准确地预测了在训练期间未遇到的空间ABM数据.
  • 该方法成功地预测了ABM行为在以前未经探索的参数范围.
  • 一个单隔间BINN引导的PDE准确地捕获了ABM动态,在传统的中场模型的位置不佳或需要多个隔间的情况下.
  • 该方法在探索参数空间方面表现出了效率.

结论:

  • 生物信息神经网络 (BINNs) 提供了一个强大的框架,用于从ABM中开发准确和可解释的DE模型.
  • 这种由BINN引导的PDE方法显著提高了跨参数空间预测和预测集体迁移动态的能力.
  • 该方法方便对ABM参数空间进行高效的探索,为基于数据的任务 (如从实验数据进行参数估计) 开辟了道路.