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在治疗影响下免疫反应和瘤演变的随机决定性建模:智能预测监督的外源网络

Hassan Raza, Muhammad Junaid Ali Asif Raja, Rikza Mubeen

IEEE transactions on computational biology and bioinformatics

|August 29, 2025

概括

这项研究使用人工智能和数学模型预测单克隆抗体和干扰素等癌症治疗如何影响免疫反应,优化治疗时间和剂量以获得更好的结果.

科学领域:

癌症学
计算生物学
免疫疗法

背景情况:

单克隆抗体和干扰素在癌症化疗中表现出协同作用.
数学模型对于理解瘤免疫力学和开发治疗方法至关重要.
预测模型可以降低药物发现成本和实验代.

研究的目的:

开发一种精确的基于人工智能的方法来了解癌症治疗的免疫反应.
预测治疗反应,优化免疫调节治疗的时间和剂量.
澄清瘤免疫相互作用和治疗功效的决定因素.

主要方法:

使用非线性自回归外源 (NARX) 网络与随机决定性差异模型相结合.
采用亚当斯数值方法来生成训练,测试和验证数据.
进行错误分析以验证框架的性能和准确性.

主要成果:

开发的框架准确地预测了癌症化疗中的免疫反应动态.
达到10^-11和10^-8之间的平均平方误差, 证明了高精度.
确定了影响瘤免疫相互作用和治疗有效性的关键决定因素.

结论:

人工智能驱动的预测模型为瘤学中的合理治疗干预提供了强有力的方法.
这种方法增强了对复杂的瘤-免疫-化疗相互作用的理解.
该框架支持优化治疗策略,有可能降低实验成本.

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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.