Recent trends of machine learning applied to multi-source data of medicinal plants
10.1016/j.jpha.2023.07.012
- Author:
Yanying ZHANG
1
;
Yuanzhong WANG
Author Information
1. Medicinal Plants Research Institute,Yunnan Academy of Agricultural Sciences,Kunming,650200,China;College of Traditional Chinese Medicine,Yunnan University of Chinese Medicine,Kunming,650500,China
- Keywords:
Machine learning;
Medicinal plant;
Multi-source data;
Data fusion;
Application
- From:
Journal of Pharmaceutical Analysis
2023;13(12):1388-1407
- CountryChina
- Language:Chinese
-
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 tradi-tional 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 out-comes 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 al-gorithms 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.
