Recent trends of machine learning applied to multi-source data of medicinal plants
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 ext...
| Main Authors: | , |
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| Format: | Article |
| Language: | English |
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Elsevier
2023-12-01
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| Series: | Journal of Pharmaceutical Analysis |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2095177923001612 |
| _version_ | 1827397093620187136 |
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| author | Yanying Zhang Yuanzhong Wang |
| author_facet | Yanying Zhang Yuanzhong Wang |
| author_sort | Yanying Zhang |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-08T19:02:18Z |
| format | Article |
| id | doaj.art-e3f104c7bfad4119a9e55e23c8eb6cf6 |
| institution | Directory Open Access Journal |
| issn | 2095-1779 |
| language | English |
| last_indexed | 2024-03-08T19:02:18Z |
| publishDate | 2023-12-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Journal of Pharmaceutical Analysis |
| spelling | doaj.art-e3f104c7bfad4119a9e55e23c8eb6cf62023-12-28T05:16:06ZengElsevierJournal of Pharmaceutical Analysis2095-17792023-12-01131213881407Recent trends of machine learning applied to multi-source data of medicinal plantsYanying Zhang0Yuanzhong Wang1Medicinal Plants Research Institute, Yunnan Academy of Agricultural Sciences, Kunming, 650200, China; College of Traditional Chinese Medicine, Yunnan University of Chinese Medicine, Kunming, 650500, ChinaMedicinal Plants Research Institute, Yunnan Academy of Agricultural Sciences, Kunming, 650200, China; Corresponding author.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.http://www.sciencedirect.com/science/article/pii/S2095177923001612Machine learningMedicinal plantMulti-source dataData fusionApplication |
| spellingShingle | Yanying Zhang Yuanzhong Wang Recent trends of machine learning applied to multi-source data of medicinal plants Journal of Pharmaceutical Analysis Machine learning Medicinal plant Multi-source data Data fusion Application |
| title | Recent trends of machine learning applied to multi-source data of medicinal plants |
| title_full | Recent trends of machine learning applied to multi-source data of medicinal plants |
| title_fullStr | Recent trends of machine learning applied to multi-source data of medicinal plants |
| title_full_unstemmed | Recent trends of machine learning applied to multi-source data of medicinal plants |
| title_short | Recent trends of machine learning applied to multi-source data of medicinal plants |
| title_sort | recent trends of machine learning applied to multi source data of medicinal plants |
| topic | Machine learning Medicinal plant Multi-source data Data fusion Application |
| url | http://www.sciencedirect.com/science/article/pii/S2095177923001612 |
| work_keys_str_mv | AT yanyingzhang recenttrendsofmachinelearningappliedtomultisourcedataofmedicinalplants AT yuanzhongwang recenttrendsofmachinelearningappliedtomultisourcedataofmedicinalplants |