ArticlePlant methods2019
Combining gene expression programming and genetic algorithm as a powerful hybrid modeling approach for pear rootstocks tissue culture media formulation.
Article in Plant methods, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.
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Who cites it
15 citing papers in PubMed.
- ADAM: advanced design and AI-driven modeling for plant tissue culture media optimization.Plant methods · 2026Article
- Hyperhydricity Syndrome in In Vitro Plants: Mechanisms, Physiology, and Control.Plants (Basel, Switzerland) · 2025Review
- Optimizing PGRs for in vitro shoot proliferation of pomegranate with bayesian-tuned ensemble stacking regression and NSGA-II: a comparative evaluation of machine learning models.Plant methods · 2024Article
- Application of Data Modeling, Instrument Engineering and Nanomaterials in Selected Medid the Scientific Recinal Plant Tissue Culture.Plants (Basel, Switzerland) · 2023Review
- A novel selection model based on multivariate methods and arbitrary genetic parameters: a case study on tomato families.Plant methods · 2023Article
- Enhancing petunia tissue culture efficiency with machine learning: A pathway to improved callogenesis.PloS one · 2023Article
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- 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
- The Past, Present and Future ofPlants (Basel, Switzerland) · 2021Review
- 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.Applied microbiology and biotechnology · 2020Article
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- Modeling and Optimizing Culture Medium Mineral Composition forFrontiers in plant science · 2020Article
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Authors and funding
8 authors.
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No grant is acknowledged in the PubMed record.
Abstract
backgroundPredicting impact of plant tissue culture media components on explant proliferation is important especially in commercial scale for optimizing efficient culture media. Previous studies have focused on predicting the impact of media components on explant growth via conventional multi-layer perceptron neural networks (MLPNN) and Multiple Linear Regression (MLR) methods. So, there is an opportunity to find more efficient algorithms such as Radial Basis Function Neural Network (RBFNN) and Gene Expression Programming (GEP). Here, a novel algorithm, i.e. GEP which has not been previously applied in plant tissue culture researches was compared to RBFNN and MLR for the first time. Pear rootstocks (Pyrodwarf and OHF) were used as case studies on predicting the effect of minerals and some hormones in the culture medium on proliferation indices.
resultsGenerally, RBFNN and GEP showed extremely higher performance accuracy than the MLR. Moreover, GEP models as the most accurate models were optimized using genetic algorithm (GA). The improvement was mainly due to the RBFNN and GEP strong estimation capability and their superior tolerance to experimental noises or improbability.
conclusionsGEP as the most robust and accurate prospecting procedure to achieve the highest proliferation quality and quantity has also the benefit of being easy to use.
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