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

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

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  • 国家科技期刊平台
  • NETL
  • NSTL
  • 万方数据
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.

Machine learningMedicinal plantMulti-source dataData fusionApplication

Yanying Zhang、Yuanzhong Wang

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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

国家自然科学基金Special Program for the Major Science and Technology Projects of Yunnan Province,ChinaSpecial Program for the Major Science and Technology Projects of Yunnan Province,China

U2202213202102AE090051-1-01202202AE090001

2023

10.1016/j.jpha.2023.07.012

药物分析学报(英文)
西安交通大学

药物分析学报(英文)

CSCD
影响因子：0.244
ISSN：2095-1779
年,卷(期)：2023.13(12)
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