Combining gene expression programming and genetic algorithm as a powerful hybrid modeling approach for pear rootstocks tissue culture media formulation
| Autor: | Mohammad Dehghani Soltani, Mohammad Mehdi Arab, Abdollatif Sheikhi, Maliheh Eftekhari, Hamed Sabzalipoor, Abbas Yadollahi, Jalal Shiri, Saeid Jamshidi |
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| Rok vydání: | 2019 |
| Předmět: |
0106 biological sciences
0301 basic medicine OHF Radial basis function neural network Computer science Plant tissue culture Plant Science lcsh:Plant culture Machine learning computer.software_genre 01 natural sciences 03 medical and health sciences Tissue culture Linear regression Genetic algorithm Genetics Gene expression programming lcsh:SB1-1110 lcsh:QH301-705.5 Multiple linear regression Artificial neural network business.industry Research Perceptron 030104 developmental biology lcsh:Biology (General) Pear rootstock Artificial intelligence business computer Pyrodwarf 010606 plant biology & botany Biotechnology Explant culture |
| Zdroj: | Plant Methods Plant Methods, Vol 15, Iss 1, Pp 1-18 (2019) |
| ISSN: | 1746-4811 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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