ArticleApplied microbiology and biotechnology2020
Introducing a hybrid artificial intelligence method for high-throughput modeling and optimizing plant tissue culture processes: the establishment of a new embryogenesis medium for chrysanthemum, as a case study.
Article in Applied microbiology and biotechnology, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 23 papers, 1 of them a synthesis that pooled it.
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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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Who cites it
23 citing papers in PubMed, 1 synthesis or guideline pooled it, 38 citations in OpenAlex.
- AGL15 Promotion of Somatic Embryogenesis: Role and Molecular Mechanism.Frontiers in plant science · 2022Pooled it
- Decoding the cannabis tissue culture puzzle: Machine learning analysis of cannabis in vitro morpho-physiological disorders expands the potential for precision micropropagation.Journal, genetic engineering & biotechnology · 2025Article
- Machine Learning-Aided Optimization of In Vitro Tetraploid Induction in Cannabis.International journal of molecular sciences · 2025Article
- Choosing an appropriate somatic embryogenesis medium of carrot (Daucus carota L.) by data mining technology.BMC biotechnology · 2024Article
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- Prediction and optimization of indirect shoot regeneration of Passiflora caerulea using machine learning and optimization algorithms.BMC biotechnology · 2023Article
- Riboflavin mediates m6A modification targeted by miR408, promoting early somatic embryogenesis in longan.Plant physiology · 2023Article
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- Artificial neural network modeling for deciphering the in vitro induced salt stress tolerance in chickpea (Physiology and molecular biology of plants : an international journal of functional plant biology · 2023Article
- Enhancing petunia tissue culture efficiency with machine learning: A pathway to improved callogenesis.PloS one · 2023Article
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- In Vitro Multiplication and Cryopreservation ofLife (Basel, Switzerland) · 2022Article
- Predictive modeling of Persian walnut (Juglans regia L.) in vitro proliferation media using machine learning approaches: a comparative study of ANN, KNN and GEP models.Plant methods · 2022Article
- Innovation in the Breeding of Common Bean Through a Combined Approach ofFrontiers in genetics · 2022Article
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- Modeling and optimizing callus growth and development in Cannabis sativa using random forest and support vector machine in combination with a genetic algorithm.Applied microbiology and biotechnology · 2021Article
- Advances and Perspectives in Tissue Culture and Genetic Engineering of Cannabis.International journal of molecular sciences · 2021Review
- Synergizing Off-Target Predictions for In Silico Insights of CENH3 Knockout in Cannabis through CRISPR/Cas.Molecules (Basel, Switzerland) · 2021Article
Corrections and comments
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Authors and funding
3 authors at 2 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Data-driven models in a combination of optimization algorithms could be beneficial methods for predicting and optimizing in vitro culture processes. This study was aimed at modeling and optimizing a new embryogenesis medium for chrysanthemum. Three individual data-driven models, including multi-layer perceptron (MLP), adaptive neuro-fuzzy inference system (ANFIS), and support vector regression (SVR), were developed for callogenesis rate (CR), embryogenesis rate (ER), and somatic embryo number (SEN). Consequently, the best obtained results were used in the fusion process by a bagging method. For medium reformulation, effects of eight ionic macronutrients on CR, ER, and SEN and effects of four vitamins on SEN were evaluated using data fusion (DF)-non-dominated sorting genetic algorithm-II (NSGA-II) and DF-genetic algorithm (GA), respectively. Results showed that DF models with the highest R
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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.