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

Tumor Immunotherapy01:27

Tumor Immunotherapy

575
Immunotherapy is a treatment that boosts or manipulates the immune system to fight diseases, including cancer. For instance, by stimulating an immune response through vaccinations against viruses that cause cancers, like hepatitis B virus and human papillomavirus, these diseases can be prevented. Nonetheless, some cancer cells can avoid the immune system due to their rapid mutation and division. The immune response to many cancers involves three phases: elimination, equilibrium, and escape.
575

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

NeuroStat: An Open-Source EEG Connectivity Platform for Randomised Controlled Trials.

Sensors (Basel, Switzerland)·2026
Same author

AHGA-SA: A Novel Adaptive Hybrid Framework for Feature Selection in IoT-Oriented Intrusion Detection with Explainable AI.

Sensors (Basel, Switzerland)·2026
Same author

Aflibercept with and without laser therapy in diabetic macular edema: a systematic review and meta-analysis.

Immunotherapy·2026
Same author

Role of long non-coding RNAs in therapeutic resistance and clinical applications in cancer.

European journal of medicinal chemistry·2026
Same author

Intelligent machine learning solutions with Bayesian regularization backpropagation adaptive networks for differential systems of Maize streak virus diseases.

Mathematical biosciences·2026
Same author

Phylogeny and diversity study in Damani sheep through mitochondrial DNA D-loop nucleotides sequence.

Tropical animal health and production·2026

相关实验视频

Updated: Jul 26, 2025

Predictive Immune Modeling of Solid Tumors
08:50

Predictive Immune Modeling of Solid Tumors

Published on: February 25, 2020

7.0K

针对非线性瘤免疫延迟模型的智能解决方案预测网络.

Nabeela Anwar1, Iftikhar Ahmad1, Adiqa Kausar Kiani2

  • 1Department of Mathematics, University of Gujrat, Gujrat, Pakistan.

Computer methods in biomechanics and biomedical engineering
|June 23, 2023
PubMed
概括

这项研究分析了一种非线性瘤免疫延迟 (TID) 模型,使用具有反向传播的神经网络来分析Levenberg-Marquardt (NNLMA). 该NNLMA有效地模拟瘤免疫动态,显示高准确性和可靠性.

关键词:
瘤免疫延迟模型延迟差分系统延迟差分系统显式的朗格-库塔方法.莱文伯格马克沃特的方法神经网络的神经网络的神经网络回归措施是回归措施.软计算范式是一种软计算范式.

更多相关视频

Author Spotlight: Unlocking Insights into the Immune Cell Landscape of Tumors
06:32

Author Spotlight: Unlocking Insights into the Immune Cell Landscape of Tumors

Published on: August 18, 2023

2.1K
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

8.3K

相关实验视频

Last Updated: Jul 26, 2025

Predictive Immune Modeling of Solid Tumors
08:50

Predictive Immune Modeling of Solid Tumors

Published on: February 25, 2020

7.0K
Author Spotlight: Unlocking Insights into the Immune Cell Landscape of Tumors
06:32

Author Spotlight: Unlocking Insights into the Immune Cell Landscape of Tumors

Published on: August 18, 2023

2.1K
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

8.3K

科学领域:

  • 数学生物学 数学生物学
  • 计算生物学 计算生物学
  • 免疫学 免疫学 免疫学

背景情况:

  • 瘤生长和免疫系统的相互作用是复杂的过程.
  • 生物系统的时间延迟显著影响模型动态.
  • 理解这些动态对于开发有效的癌症疗法至关重要.

研究的目的:

  • 分析非线性瘤免疫延迟 (TID) 模型的动态.
  • 应用软计算范式,特别是具有反向传播的神经网络莱文伯格-马奎特方法 (NNLMA),用于解决TID模型.
  • 验证NNLMA在准确表示瘤免疫相互作用方面的有效性.

主要方法:

  • 使用延迟普通微分方程制定非线性TID模型.
  • 使用明确的Runge-Kutta方法 (RKM) 生成基线数据.
  • 应用NNLMA与数据分类用于培训,测试和验证.

主要成果:

  • 该NNLMA成功地近似了非线性TID模型的解决方案.
  • 通过微不足道的绝对错误证明了强度,可靠性和有效性.
  • 通过平均平方误差和回归的最佳建模指数来表示的高准确度.

结论:

  • NNLMA是一个强大而有效的计算工具,用于分析非线性TID模型.
  • 这项研究验证了软计算方法在理解复杂的生物动态中的使用.
  • 结果支持NNLMA在瘤免疫学和计算建模方面的未来研究的潜力.