用可解释神经网络和博弈论提升癌症个性化治疗的精准与可信度
Kolmogorov-Arnold Networks and Evolutionary Game Theory for More Personalized Cancer Treatment
- 结合柯尔莫哥洛夫-阿诺德网络与演化博弈理论构建新框架
- 实现对肿瘤进展和治疗反应的动态建模，提升预测准确性
- 适合关注可解释人工智能与精准医疗交叉研究者
个性化癌症治疗正通过精准医学和先进计算技术重塑肿瘤学。尽管潜力巨大，当前模型仍面临泛化能力差、可解释性不足和可复现性低等问题，多依赖难以理解的黑箱机器学习模型，阻碍其临床应用。本文提出一种融合柯尔莫哥洛夫-阿诺德网络（KANs）与演化博弈理论（EGT）的新框架。基于柯尔莫哥洛夫-阿诺德表示定理，KANs 提供可解释的、基于边的神经架构，能以高适应性建模复杂生物系统。将其嵌入 EGT 框架后，可动态模拟癌症进展与治疗响应。该混合方法结合了 KAN 的计算精度与 EGT 的机制洞察，有望显著提升预测准确率、可扩展性与临床可用性。
原文摘要 · Abstract (English)
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.
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