相关实验视频

Updated: Jul 26, 2025

Predictive Immune Modeling of Solid Tumors

Predictive Immune Modeling of Solid Tumors

Published on: February 25, 2020

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

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.

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.

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

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

Published on: August 18, 2023

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

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

Published on: October 10, 2018

相关概念视频

Tumor Immunotherapy

Tumor Immunotherapy

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.