Article Details

Abstract

Traditional Chinese Medicine (TCM) dose-effect data typically feature small sample sizes, strong correlations among variables, and complex nonlinear relationships, making it difficult for traditional multivariate regression methods to meet the corresponding analytical requirements. To address these problems, this paper integrates the respective advantages of Kernel Principal Component Analysis (KPCA) and Partial Least Squares (PLS) and proposes a feature-weighted mixed-kernel KPCA-PLS regression method. First, the method uses Partial Least Squares-Variable Importance in Projection (PLS-VIP) to measure feature importance and calculate feature weights. These weights are incorporated into the subsequent construction of kernel functions to enhance the model’s sensitivity to key variables. Second, a mixed-kernel learning structure combining a Radial Basis Function (RBF) kernel and a Polynomial (Poly) kernel is constructed in KPCA to extract principal components, thereby accounting for both local and global feature extraction capabilities. The principal components extracted by KPCA are then input into the PLS model for regression. A multi-population genetic algorithm is introduced to automatically optimize combinations of kernel-function parameters during iteration and obtain optimal regression performance. The proposed method is compared with six other methods on two TCM datasets and three public datasets, and the results demonstrate its superiority.

Keywords

TCM dose-effect data; feature weighting; KPCA; PLS

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Funding

National Natural Science Foundation of China (82260849); Development Plan for University level Science and Technology Innovation Team of Jiangxi University of Traditional Chinese Medicine (CXTD22015)

About the authors

Li Xiang (2001-), male, Han nationality, from Tongling, Anhui, master's student, research direction: pharmaceutical data mining; Corresponding author: Nie Bin (1972-), male, Han ethnicity, from Xiajiang, Jiangxi Province, professor, master's supervisor, doctoral student, research direction: pharmaceutical data mining, traditional Chinese medicine informatics, traditional Chinese medicine; Zheng Shuifei (1998-), male, Han nationality, from Shangrao, Jiangxi, master's student, research direction: pharmaceutical data mining; Zhang Zhiyao (2001-), female, Han ethnicity, born in Nanchang, Jiangxi Province, is a master's student with a research focus on pharmaceutical data mining; Meng Chong (2003-), male, Han ethnicity, from Fuyang, Anhui Province, is a master's student with a research focus on pharmaceutical data mining; Shi Chenhao (2001-), male, Han ethnicity, from Nantong, Jiangsu, is a master's student with a research focus on pharmaceutical data mining.