ArticlePlant methods2020
Development of support vector machine-based model and comparative analysis with artificial neural network for modeling the plant tissue culture procedures: effect of plant growth regulators on somatic embryogenesis of chrysanthemum, as a case study.
Article in Plant methods, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 38 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
38 citing papers in PubMed, 97 citations in OpenAlex.
- Artificial Intelligence Methods in Forest Biotechnology: Current Status and Future Prospects.International journal of molecular sciences · 2026Review
- ADAM: advanced design and AI-driven modeling for plant tissue culture media optimization.Plant methods · 2026Article
- A GWAS-machine learning framework reveals protein-synthesis pathway signals for yield in Theobroma cacao after population-structure correction.Scientific reports · 2026Article
- Enhancing the pectolinarigenin production inFrontiers in plant science · 2026Article
- Unlocking regeneration potential: harnessing morphogenic regulators and small peptides for enhanced plant engineering.The Plant journal : for cell and molecular biology · 2025Review
- AI-driven advances in plant biotechnology: sharpening the edge of plant tissue culture and genome editing.Frontiers in plant science · 2025Review
- Application of machine learning algorithms for predicting the life-long physiological effects of zinc oxide Micro/Nano particles on Carum copticum.BMC plant biology · 2024Article
- Choosing an appropriate somatic embryogenesis medium of carrot (Daucus carota L.) by data mining technology.BMC biotechnology · 2024Article
- Article
- Leveraging machine learning to unravel the impact of cadmium stress on goji berry micropropagation.PloS one · 2024Article
- Prediction and optimization of indirect shoot regeneration of Passiflora caerulea using machine learning and optimization algorithms.BMC biotechnology · 2023Article
- Application of machine learning algorithms and feature selection in rapeseed (Brassica napus L.) breeding for seed yield.Plant methods · 2023Article
- Application of Data Modeling, Instrument Engineering and Nanomaterials in Selected Medid the Scientific Recinal Plant Tissue Culture.Plants (Basel, Switzerland) · 2023Review
- Phytochemical Composition and Biological Activities of Extracts from Early, Mature, and Germinated Somatic Embryos ofPlants (Basel, Switzerland) · 2023Article
- Enhancing petunia tissue culture efficiency with machine learning: A pathway to improved callogenesis.PloS one · 2023Article
- Artificial intelligence-driven systems engineering for next-generation plant-derived biopharmaceuticals.Frontiers in plant science · 2023Review
- Review
- Modeling and optimizing in vitro percentage and speed callus induction of carrot via Multilayer Perceptron-Single point discrete GA and radial basis function.BMC biotechnology · 2022Article
- A model for genuineness detection in genetically and phenotypically similar maize variety seeds based on hyperspectral imaging and machine learning.Plant methods · 2022Article
- Machine-Learning-Based Genome-Wide Association Studies for Uncovering QTL Underlying Soybean Yield and Its Components.International journal of molecular sciences · 2022Article
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 3 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundOptimizing the somatic embryogenesis protocol can be considered as the first and foremost step in successful gene transformation studies. However, it is usually difficult to achieve an optimized embryogenesis protocol due to the cost and time-consuming as well as the complexity of this process. Therefore, it is necessary to use a novel computational approach, such as machine learning algorithms for this aim. In the present study, two machine learning algorithms, including Multilayer Perceptron (MLP) as an artificial neural network (ANN) and support vector regression (SVR), were employed to model somatic embryogenesis of chrysanthemum, as a case study, and compare their prediction accuracy.
resultsThe results showed that SVR (R
conclusionsSVR-NSGA-II can be employed as a reliable and applicable computational methodology in future plant tissue culture studies.
Indexed as
Identifiers
What OpenQuestion holds
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.