用于更个性化癌症治疗的柯尔莫哥洛夫-阿诺德网络与演化博弈论
Kolmogorov-Arnold Networks and Evolutionary Game Theory for More Personalized Cancer Treatment
AI总结：
针对当前癌症治疗预测模型可解释性不足等临床应用瓶颈，提出融合KAN与EGT的混合框架，兼顾计算精度与机制洞见，以提升模型预测效果与临床可用性。
摘要：
个性化癌症治疗正借助精准医学与先进计算技术为患者定制疗法，彻底革新肿瘤学领域。尽管其具有变革性潜力，但预测模型存在泛化性、可解释性和可复现性有限等挑战，阻碍了其融入临床实践。当前方法常依赖黑箱机器学习模型，这类模型虽具准确性，却缺乏临床医生信任与实际应用所需的透明度。本文提出构建一个融合Kolmogorov-Arnold Networks（KANs，柯尔莫哥洛夫-阿诺德网络）与Evolutionary Game Theory（EGT，演化博弈论）的创新框架，以解决上述局限。受柯尔莫哥洛夫-阿诺德表示定理启发，KANs提供了基于边的可解释神经架构，能以前所未有的适应性对复杂生物系统建模。将其整合至EGT框架可实现癌症进展与治疗应答的动态建模。通过结合KAN的计算精度与EGT的机制性洞见，这种混合方法有望提升预测准确性、可扩展性与临床可用性。
英文摘要：
Personalized cancer treatment is revolutionizing oncology by leveraging precision medicine and advanced computational techniques to tailor therapies to individual patients. Despite its transformative potential, challenges such as limited generalizability, interpretability, and reproducibility of predictive models hinder its integration into clinical practice. Current methodologies often rely on black-box machine learning models, which, while accurate, lack the transparency needed for clinician trust and real-world application. This paper proposes the development of an innovative framework that bridges Kolmogorov-Arnold Networks (KANs) and Evolutionary Game Theory (EGT) to address these limitations. Inspired by the Kolmogorov-Arnold representation theorem, KANs offer interpretable, edge-based neural architectures capable of modeling complex biological systems with unprecedented adaptability. Their integration into the EGT framework enables dynamic modeling of cancer progression and treatment responses. By combining KAN's computational precision with EGT's mechanistic insights, this hybrid approach promises to enhance predictive accuracy, scalability, and clinical usability.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
