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

文献类型： 外文期刊

第一作者： Zhang, Yanying

作者： Zhang, Yanying;Wang, Yuanzhong;Zhang, Yanying

作者机构：

关键词： Machine learning; Medicinal plant; Multi-source data; Data fusion; Application

期刊名称：JOURNAL OF PHARMACEUTICAL ANALYSIS （ 影响因子：8.8； 五年影响因子：7.2 ）

ISSN： 2095-1779

年卷期： 2023 年 13 卷 12 期

页码：

收录情况： SCI

摘要： 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. (c) 2023 The Author(s). Published by Elsevier B.V. on behalf of Xi'an Jiaotong University. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).

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