Recent trends of machine learning applied to multi-source data of medicinal plants

Yanying Zhang; Yuanzhong Wang

doi:10.1016/j.jpha.2023.07.012

Volume 13 Issue 12

Dec. 2023

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Article Navigation > Journal of Pharmaceutical Analysis > 2023 > 13(12): 1388-1407

Yanying Zhang, Yuanzhong Wang. Recent trends of machine learning applied to multi-source data of medicinal plants[J]. Journal of Pharmaceutical Analysis, 2023, 13(12): 1388-1407. doi: 10.1016/j.jpha.2023.07.012

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Yanying Zhang, Yuanzhong Wang. Recent trends of machine learning applied to multi-source data of medicinal plants[J]. Journal of Pharmaceutical Analysis, 2023, 13(12): 1388-1407. doi: 10.1016/j.jpha.2023.07.012

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Recent trends of machine learning applied to multi-source data of medicinal plants

doi: 10.1016/j.jpha.2023.07.012

Yanying Zhang^a,b,
Yuanzhong Wang^{a
,
,}

a. Medicinal Plants Research Institute, Yunnan Academy of Agricultural Sciences, Kunming, 650200, China;
b. College of Traditional Chinese Medicine, Yunnan University of Chinese Medicine, Kunming, 650500, China

Funds:

This work was supported by the National Natural Science Foundation of China (Grant No.: U2202213), and the Special Program for the Major Science and Technology Projects of Yunnan Province, China (Grant Nos.: 202102AE090051-1-01, and 202202AE090001).

Received Date: Apr. 27, 2023
Accepted Date: Jul. 19, 2023
Rev Recd Date: Jul. 17, 2023
Publish Date: Jul. 25, 2023

Abstract

Abstract

In traditional medicine and ethnomedicine, medicinal plants have long been recognized as the basis for materials in therapeutic applications worldwide. In particular, the remarkable curative effect of traditional Chinese medicine during corona virus disease 2019 (COVID-19) pandemic has attracted extensive attention globally. Medicinal plants have, therefore, become increasingly popular among the public. However, with increasing demand for and profit with medicinal plants, commercial fraudulent events such as adulteration or counterfeits sometimes occur, which poses a serious threat to the clinical outcomes and interests of consumers. With rapid advances in artificial intelligence, machine learning can be used to mine information on various medicinal plants to establish an ideal resource database. We herein present a review that mainly introduces common machine learning algorithms and discusses their application in multi-source data analysis of medicinal plants. The combination of machine learning algorithms and multi-source data analysis facilitates a comprehensive analysis and aids in the effective evaluation of the quality of medicinal plants. The findings of this review provide new possibilities for promoting the development and utilization of medicinal plants.
- Machine learning,
- Medicinal plant,
- Multi-source data,
- Data fusion,
- Application

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