Jove
Visualize
联系我们
JoVE
x logofacebook logolinkedin logoyoutube logo
关于 JoVE
概览领导团队博客JoVE 帮助中心
作者
出版流程编辑委员会范围与政策同行评审常见问题投稿
图书馆员
用户评价订阅访问资源图书馆顾问委员会常见问题
研究
JoVE JournalMethods CollectionsJoVE Encyclopedia of Experiments存档
教育
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab Manual教师资源中心教师网站
使用条款与条件
隐私政策
政策

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...

您也可能阅读

相关文章

通过共同作者、期刊和引用图与本文相关的文章。

排序
Same author

Surrounding rock stability and engineering practice of the 52607 working face approaching a fault-protected coal pillar.

Scientific reports·2026
Same author

Middle Ear Bacterial Colonization and Recurrence of Radiation-Induced OME: A Prospective Study.

The Laryngoscope·2026
Same author

Study on the influence of stress relief in close-distance coal seam Goaf and roadway excavation disturbance on surrounding rock.

Scientific reports·2026
Same author

SEIRV epidemiological model for COVID 19 with Holling type II functional response.

Scientific reports·2026
Same author

Diketopiperazines from marine-derived <i>Exserohilum</i> sp. M1-6.

Natural product research·2026
Same author

Identification of Genetic Risk Factors Associated With Herbal and Dietary Supplement-Induced Acute Liver Failure Using Whole Exome Sequencing Analysis.

Gastro hep advances·2025

相关实验视频

Updated: Jul 18, 2026

Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma
04:09

Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma

Published on: October 10, 2018

8.2K

在深度学习时代,用于预测肝毒性的计算模型.

Fahad Mostafa1,2, Minjun Chen2

  • 1Department of Mathematics and Statistics, Texas Tech University, Lubbock, TX, United States.

Frontiers in toxicology
|February 5, 2024
PubMed
概括

深度学习 (DL) 提高了使用定量结构-活性关系 (QSAR) 模型的药物诱导性肝损伤 (DILI) 预测. 这种方法提供了快速的,早期查DILI风险,提高人类的安全.

科学领域:

  • 药理学和毒理学 药理学和毒理学
  • 计算化学计算化学
  • 人工智能在医学中的应用

背景情况:

  • 药物诱导性肝损伤 (DILI) 是一个关键的安全问题,可能导致严重的结果,包括肝衰竭和死亡.
  • 定量结构-活性关系 (QSAR) 模型对于早期肝毒性查至关重要,因为它们的非物理物质要求和速度.
  • 深度学习 (DL) 的最新进展使复杂的QSAR模型的开发成为可能.

研究的目的:

  • 审查深度学习 (DL) 在预测药物诱导性肝损伤 (DILI) 的应用.
  • 专注于开发QSAR模型,利用广泛的化学结构数据集和DILI结果.
  • 为了评估DL方法与传统的机器学习 (ML) 方法对DILI预测.

主要方法:

  • 对用于DILI预测的深度学习 (DL) 方法的全面审查.
  • 分析使用化学结构数据和DILI结果开发的QSAR模型.
  • 对DL技术与传统机器学习 (ML) 方法进行比较评估.

主要成果:

  • 深度学习 (DL) 模型显示了提高DILI预测的准确性和效率的巨大潜力.
  • 对比强调了DL技术在解释性,可扩展性和通用性方面的优势和局限性.
关键词:
深度学习是一种深度学习.药物安全 药物安全药物引起的肝损伤 (DILI)机器学习是机器学习.预测模型是一个预测模型.

更多相关视频

Advanced 3D Liver Models for In vitro Genotoxicity Testing Following Long-Term Nanomaterial Exposure
08:25

Advanced 3D Liver Models for In vitro Genotoxicity Testing Following Long-Term Nanomaterial Exposure

Published on: June 5, 2020

6.8K
Human Liver Microphysiological System for Assessing Drug-Induced Liver Toxicity In Vitro
11:06

Human Liver Microphysiological System for Assessing Drug-Induced Liver Toxicity In Vitro

Published on: January 31, 2022

4.4K

相关实验视频

Last Updated: Jul 18, 2026

Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma
04:09

Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma

Published on: October 10, 2018

8.2K
Advanced 3D Liver Models for In vitro Genotoxicity Testing Following Long-Term Nanomaterial Exposure
08:25

Advanced 3D Liver Models for In vitro Genotoxicity Testing Following Long-Term Nanomaterial Exposure

Published on: June 5, 2020

6.8K
Human Liver Microphysiological System for Assessing Drug-Induced Liver Toxicity In Vitro
11:06

Human Liver Microphysiological System for Assessing Drug-Induced Liver Toxicity In Vitro

Published on: January 31, 2022

4.4K
  • 基于DL的QSAR模型为早期肝毒性查提供了一个有希望的途径.

    • 结论:

    • 深度学习方法有望显著改善DILI风险预测.
    • 未来的研究应该专注于利用DL来获得强大的预测模型,以减轻人类的DILI.
    • 增强的预测模型可以促进更安全的药物开发和改善患者的治疗结果.