ArticlePlant methods2021
Prediction of hypericin content in Hypericum perforatum L. in different ecological habitat using artificial neural networks.
Article in Plant methods, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 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.
The trial behind it
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
8 citing papers in PubMed, 40 citations in OpenAlex.
- Simulation of emitter discharge along drip laterals under drip fertigation system using artificial neural network.PloS one · 2025Article
- Multilayer perceptron and support vector regression models for feline parturition date prediction.Heliyon · 2024Article
- Antimicrobial activity of D-amino acid in combination with photo-sonoactivated hypericin nanoparticles against Acinetobacter baumannii.BMC microbiology · 2023Article
- Clinical decision support system for quality of life among the elderly: an approach using artificial neural network.BMC medical informatics and decision making · 2022Article
- Does phenotyping ofFrontiers in plant science · 2022Review
- Bayesian modeling for pro-environmental behavior data: sorting and selecting relevant variables.Stochastic environmental research and risk assessment : research journal · 2022Article
- A comparison of artificial intelligence techniques for predicting hyperforin content inPlant direct · 2021Article
- Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors at 4 institutions in 3 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundHypericum is an important genus in the family Hypericaceae, which includes 484 species. This genus has been grown in temperate regions and used for treating wounds, eczema and burns. The aim of this study was to predict the content of hypericin in Hypericum perforatum in varied ecological and phenological conditions of habitat using artificial neural network techniques [MLP (Multi-Layer Perceptron), RBF (Radial Basis Function) and SVM (Support Vector Machine)].
resultsAccording to the results, the MLP model (R
conclusionsThe designed graphical user interface will help pharmacognosist, manufacturers and producers of medicinal plants and so on to run the MLP model on new data to easily discover the content of hypericin in H. perforatum by entering ecological conditions of site, soil characteristics and plant phenological stages.
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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 OpenQuestion graph.