ReviewJournal of pharmaceutical analysis2023
Recent trends of machine learning applied to multi-source data of medicinal plants.
Review in Journal of pharmaceutical analysis, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 20 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
20 citing papers in PubMed.
- Emerging natural product development strategies drive the therapeutic potential exploration and clinical translation of hydroxysafflor yellow A.Journal of pharmaceutical analysis · 2026Review
- An integrated machine learning framework for TCM five-flavor classification based on E-tongue profiling and SHAP analysis.Chinese medicine · 2026Article
- Deep Learning-Based and Python-Driven Construction and Application of a Mass Spectrometry Data Analysis Workflow: Taking Glucosinolates as an Example.Metabolites · 2026Article
- Integrating metabolomics and machine learning to forecast anti-inflammatory and antioxidant activities in D. officinale leaves.Chinese medicine · 2026Article
- Review
- Sample preparation techniques for quality evaluation and safety control of medicinal and edible plants: Overview, advances, applications, and future perspectives.Journal of pharmaceutical analysis · 2025Review
- CISCS: Classification of inter-class similarity based medicinal plant species groups with machine learning.MethodsX · 2025Article
- Innovative Approach in Nursing Care: Artificial Intelligence-Assisted Incentive Spirometry.Healthcare (Basel, Switzerland) · 2025Article
- Effect of Phenolic Compounds and Terpenes on the Flavour and Functionality of Plant-Based Foods.Nutrients · 2025Review
- The integration of machine learning into traditional Chinese medicine.Journal of pharmaceutical analysis · 2025Review
- FT-NIR Spectra of Different Dimensions Combined with Machine Learning and Image Recognition for Origin Identification: An Example ofACS omega · 2025Article
- Application of spectroscopic technology with machine learning in Chinese herbs from seeds to medicinal materials: The case of genusJournal of pharmaceutical analysis · 2025Review
- Modelling key ecological factors influencing the distribution and content of silymarin antioxidant in Silybum marianum L.PloS one · 2025Article
- Digital intelligence technology: new quality productivity for precision traditional Chinese medicine.Frontiers in pharmacology · 2025Review
- Application and research progress on artificial intelligence in the quality of Traditional Chinese Medicine.Frontiers in pharmacology · 2025Review
- Microbiota and urinary tumor immunity: Mechanisms, therapeutic implications, and future perspectives.Chinese journal of cancer research = Chung-kuo yen cheng yen chiu · 2024Article
- Application of ATR-FTIR and FT-NIR spectroscopy coupled with chemometrics for species identification and quality prediction of boletes.Food chemistry: X · 2024Article
- Influence of seasonality and habitat on chemical composition, cytotoxicity and antimicrobial properties of theHeliyon · 2024Article
- Article
- Effect of drying temperature on composition of edible mushrooms: Characterization and assessment via HS-GC-MS and IR spectral based volatile profiling and chemometrics.Current research in food science · 2024Article
Corrections and comments
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Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
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.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.