Dose-effect relationship analysis of TCM based on deep Boltzmann machine and partial least squares

Wangping Xiong; Yimin Zhu; Qingxia Zeng; Jianqiang Du; Kaiqi Wang; Jigen Luo; Ming Yang; Xian Zhou; Wangping Xiong; Yimin Zhu; Qingxia Zeng; Jianqiang Du; Kaiqi Wang; Jigen Luo; Ming Yang; Xian Zhou

doi:10.3934/mbe.2023644

Mathematical Biosciences and Engineering

2023, Volume 20, Issue 8: 14395-14413. doi: 10.3934/mbe.2023644

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Dose-effect relationship analysis of TCM based on deep Boltzmann machine and partial least squares

1.
School of Computer, Jiangxi University of Chinese Medicine, Nanchang 330004, China
2.
Key Laboratory of Modern Preparations Chinese Medicine, Jiangxi University of Chinese Medicine, Nanchang 330004, China

Academic Editor: Giuseppe Ciaburro

Received: 04 May 2023 Revised: 04 June 2023 Accepted: 11 June 2023 Published: 30 June 2023

A dose-effect relationship analysis of traditional Chinese Medicine (TCM) is crucial to the modernization of TCM. However, due to the complex and nonlinear nature of TCM data, such as multicollinearity, it can be challenging to conduct a dose-effect relationship analysis. Partial least squares can be applied to multicollinearity data, but its internally extracted principal components cannot adequately express the nonlinear characteristics of TCM data. To address this issue, this paper proposes an analytical model based on a deep Boltzmann machine (DBM) and partial least squares. The model uses the DBM to extract nonlinear features from the feature space, replaces the components in partial least squares, and performs a multiple linear regression. Ultimately, this model is suitable for analyzing the dose-effect relationship of TCM. The model was evaluated using experimental data from Ma Xing Shi Gan Decoction and datasets from the UCI Machine Learning Repository. The experimental results demonstrate that the prediction accuracy of the model based on the DBM and partial least squares method is on average 10% higher than that of existing methods.
- deep Boltzmann machine,
- deep learning,
- partial least squares,
- traditional Chinese medicine,
- drug dose-effect relationships
Citation: Wangping Xiong, Yimin Zhu, Qingxia Zeng, Jianqiang Du, Kaiqi Wang, Jigen Luo, Ming Yang, Xian Zhou. Dose-effect relationship analysis of TCM based on deep Boltzmann machine and partial least squares[J]. Mathematical Biosciences and Engineering, 2023, 20(8): 14395-14413. doi: 10.3934/mbe.2023644

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Abstract

A dose-effect relationship analysis of traditional Chinese Medicine (TCM) is crucial to the modernization of TCM. However, due to the complex and nonlinear nature of TCM data, such as multicollinearity, it can be challenging to conduct a dose-effect relationship analysis. Partial least squares can be applied to multicollinearity data, but its internally extracted principal components cannot adequately express the nonlinear characteristics of TCM data. To address this issue, this paper proposes an analytical model based on a deep Boltzmann machine (DBM) and partial least squares. The model uses the DBM to extract nonlinear features from the feature space, replaces the components in partial least squares, and performs a multiple linear regression. Ultimately, this model is suitable for analyzing the dose-effect relationship of TCM. The model was evaluated using experimental data from Ma Xing Shi Gan Decoction and datasets from the UCI Machine Learning Repository. The experimental results demonstrate that the prediction accuracy of the model based on the DBM and partial least squares method is on average 10% higher than that of existing methods.

References

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