基于深度博尔兹曼机器和部分最小平方的TCM的剂量效应关系分析
Wangping Xiong1,2, Yimin Zhu1, Qingxia Zeng1
1School of Computer, Jiangxi University of Chinese Medicine, Nanchang 330004, China.
Mathematical biosciences and engineering : MBE
|September 7, 2023
概括
分析传统中医药 (TCM) 的剂量效应关系至关重要. 一种新的深博兹曼机器 (DBM) 和部分最小方程模型将复杂的TCM数据的预测精度提高10%.
科学领域:
- 药理学 药理学是指药理学的学科.
- 计算生物学 计算生物学
- 传统中国医药 传统中国医药
背景情况:
- 现代化传统中医 (TCM) 需要强大的剂量效应关系分析.
- 传统医疗医学数据带来了诸如多线性和非线性等挑战,阻碍了传统的统计方法.
- 现有的方法,如部分最小平方 (PLS),难以捕捉TCM数据固有的非线性特征.
研究的目的:
- 为TCM剂量效应关系分析提出一种新的分析模型.
- 解决处理复杂和非线性TCM数据的现有方法的局限性.
- 为了提高预测TCM剂量效果关系的准确性.
主要方法:
- 开发一个分析模型,将一个深波兹曼机器 (DBM) 与部分最小平方 (PLS) 集成在一起.
- 使用DBM从TCM数据的特征空间中提取非线性特征.
- 用DBM提取的特征替换传统的PLS组件,用于多重线性回归.
主要成果:
- 拟议的DBM-PLS模型在分析TCM剂量效应关系方面表现出卓越的表现.
- 使用Ma Xing Shi Gan Decoction数据和UCI机器学习存储库数据集进行实验验证.
- 与现有分析方法相比,平均预测准确度提高了10%.
结论:
- DBM-PLS模型有效地捕捉了TCM数据的非线性特征.
- 这种新的方法为TCM现代化和数据分析提供了重大进展.
- 该模型为了解TCM剂量效应动态提供了更准确,更可靠的工具.
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