设计一种新的辐射基础神经结构,用于解决动态C型肝炎病毒模型
Zulqurnain Sabir1, Adilkazy Yessengaliyev2, Abdikhalyk Temirzhan2
1Department of Computer Science and Mathematics, Lebanese American University, Beirut, Lebanon.
Scientific reports
|December 21, 2025
概括
这项研究引入了一种新的辐射基础神经网络,以准确地模拟高病毒载荷患者的C型肝炎病毒 (HCV) 动态. 该方法在了解HCV感染和治疗方面实现了高精度.
科学领域:
- 计算生物学是一种计算生物学.
- 数学建模的数学建模
- 病毒学 病毒学
背景情况:
- 肝炎C病毒 (HCV) 感染动态涉及病毒,感染和未感染的肝细胞之间的复杂相互作用.
- 了解这些动态对于开发有效的治疗策略至关重要,特别是在基线病毒载量高的患者中.
- 非线性动态系统通常用于表示像病毒感染这样的生物过程.
研究的目的:
- 设计和实施一种新的辐射基础神经网络 (RBNN),用于解决动态C型肝炎病毒模型.
- 为解决高基线病毒载量患者中HCV感染的非线性动态结构.
- 为了优化RBNN模型,使用贝叶斯规范化方案来提高准确性.
主要方法:
- 使用了一个带有辐射基函数和20个神经元的前神经网络.
- 贝叶斯规范化方法用于优化.
- 使用显式Runge-Kutta方法生成了一个参考溶液,其步骤大小为0.01.
- 数据被分为培训 (72%),验证 (14%) 和测试 (14%) 集.
主要成果:
- 该RBNN模型在解决HCV动态模型方面表现出高精度.
- 实现的绝对误差值在10-06到10-08之间.
- 结果显示了良好的重叠,表明了拟议方案的可靠性.
结论:
- 开发的RBNN与贝叶斯规范化是非线性动态C型肝炎病毒模型的有效和精确解决方案.
- 该方法为分析HCV感染动态提供了强大的工具,特别是在高病毒载量场景中.
- 统计评估证实了拟议的计算方法的效率和准确性.
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