HerbMet: Enhancing metabolomics data analysis for accurate identification of Chinese herbal medicines using deep learning
Y Sha, M Jiang, G Luo, W Meng, X Zhai… - Phytochemical …, 2024 - Wiley Online Library
Phytochemical Analysis, 2024•Wiley Online Library
Introduction Chinese herbal medicines have been utilized for thousands of years to prevent
and treat diseases. Accurate identification is crucial since their medicinal effects vary
between species and varieties. Metabolomics is a promising approach to distinguish herbs.
However, current metabolomics data analysis and modeling in Chinese herbal medicines
are limited by small sample sizes, high dimensionality, and overfitting. Objectives This study
aims to use metabolomics data to develop HerbMet, a high‐performance artificial …
and treat diseases. Accurate identification is crucial since their medicinal effects vary
between species and varieties. Metabolomics is a promising approach to distinguish herbs.
However, current metabolomics data analysis and modeling in Chinese herbal medicines
are limited by small sample sizes, high dimensionality, and overfitting. Objectives This study
aims to use metabolomics data to develop HerbMet, a high‐performance artificial …
Introduction
Chinese herbal medicines have been utilized for thousands of years to prevent and treat diseases. Accurate identification is crucial since their medicinal effects vary between species and varieties. Metabolomics is a promising approach to distinguish herbs. However, current metabolomics data analysis and modeling in Chinese herbal medicines are limited by small sample sizes, high dimensionality, and overfitting.
Objectives
This study aims to use metabolomics data to develop HerbMet, a high‐performance artificial intelligence system for accurately identifying Chinese herbal medicines, particularly those from different species of the same genus.
Methods
We propose HerbMet, an AI‐based system for accurately identifying Chinese herbal medicines. HerbMet employs a 1D‐ResNet architecture to extract discriminative features from input samples and uses a multilayer perceptron for classification. Additionally, we design the double dropout regularization module to alleviate overfitting and improve model's performance.
Results
Compared to 10 commonly used machine learning and deep learning methods, HerbMet achieves superior accuracy and robustness, with an accuracy of 0.9571 and an F1‐score of 0.9542 for distinguishing seven similar Panax ginseng species. After feature selection by 25 different feature ranking techniques in combination with prior knowledge, we obtained 100% accuracy and an F1‐score for discriminating P. ginseng species. Furthermore, HerbMet exhibits acceptable inference speed and computational costs compared to existing approaches on both CPU and GPU.
Conclusions
HerbMet surpasses existing solutions for identifying Chinese herbal medicines species. It is simple to use in real‐world scenarios, eliminating the need for feature ranking and selection in classical machine learning‐based methods.
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