标题： 基于人工智能的混合生态模型用于预测溶瘤病毒治疗动力学 显示英文标题

标题： AI-Driven Hybrid Ecological Model for Predicting Oncolytic Viral Therapy Dynamics

摘要： 溶瘤病毒治疗（OVT）是一种针对侵袭性和复发性癌症的新兴精准治疗方式。 然而，其临床疗效受到肿瘤-病毒-免疫相互作用复杂性以及缺乏个性化治疗预测模型的阻碍。 本研究开发了一个数据驱动、人工智能增强的计算模型，结合了具有时间延迟的广义洛特卡-沃尔泰拉方程与先进的优化算法，包括遗传算法、差分进化和强化学习，以优化OVT振荡的增长和阻尼。 我们假设该模型可以在提供准确的实时OVT反应预测的同时，识别关键生物标志物以提高治疗效果。 该模型表现出强大的预测准确性，达到均方误差（MSE）< 0.02且R平方> 0.82。 尽管该模型在因果关系上是中立的，并不了解具体的实验条件或治疗组合，但它仍能识别出经实验验证的生物标志物，如TNF、NFkB、CD81、TRAF2、IL18和BID，以及其他炎症细胞因子和细胞外基质重建因子。 基因集富集分析证实了这些生物标志物是肿瘤进展的关键预测因子，并表明光动力疗法激活的免疫反应与联合OVT和免疫检查点抑制剂引发的反应类似。 这种混合模型代表了向精准肿瘤学和计算医学迈出的重要一步，能够实现纵向、自适应的治疗方案，并基于分子特征开发靶向免疫疗法，有望改善患者预后。 显示英文摘要

摘要： Oncolytic viral therapy (OVT) is an emerging precision therapy for aggressive and recurrent cancers. However, its clinical efficacy is hindered by the complexity of tumor-virus-immune interactions and the lack of predictive models for personalized treatment. This study develops a data-driven, AI-powered computational model combining time-delayed Generalized Lotka-Volterra equations with advanced optimization algorithms, including Genetic Algorithms, Differential Evolution, and Reinforcement Learning, to optimize OVT oscillations' growth and damping. We hypothesize that the model can provide accurate, real-time predictions of OVT responses while identifying key biomarkers to enhance therapeutic efficacy. The model demonstrates strong predictive accuracy, achieving mean squared error (MSE) < 0.02 and R-squared > 0.82. It also identifies experimentally validated biomarkers such as TNF, NFkB, CD81, TRAF2, IL18, and BID, among other inflammatory cytokines and extracellular matrix reconstruction factors, despite being causally agnostic and unaware of specific experimental conditions or therapeutic combinations. Gene set enrichment analysis confirmed these biosignatures as critical predictors of tumor progression and indicated that photodynamic therapy activates immune responses similar to those elicited by combined OVT and immune checkpoint inhibitors. This hybrid model represents a significant step toward precision oncology and computational medicine, enabling longitudinal, adaptive treatment regimens and developing targeted immunotherapies based on molecular signatures, potentially improving patient outcomes.