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A hybrid model based on general regression neural network and fruit fly optimization algorithm for forecasting and optimizing paclitaxel biosynthesis in

机译：一种基于一般回归神经网络的混合模型及果蝇优化算法预测和优化紫杉醇生物合成

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Scatter plot of actual data against predicted values of dry weight, intracellular (µg g−1DW), intracellular (µg l−1), extracellular and total yield of paclitaxel, and extracellular paclitaxel portion in Corylus avellana cell cultures using general regression neural network-fruit fly optimization algorithm (GRNN-FOA) models in training subset. The solid line shows fitted simple regression line on scatter points

机译：将实际数据散射与预测值的干重，细胞内（μgg-1dw），紫杉醇细胞外和紫杉醇的细胞外和总收率的预测值，以及使用一般回归神经网络的植物植物细胞培养物中的细胞外紫杉醇部分 - 果蝇优化算法（GRNN-FOA）训练子集模型。实线显示了散点点的拟合简单回归线

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期刊名称 Plant Methods
作者
Mina Salehi; Siamak Farhadi; Ahmad Moieni; Naser Safaie; Mohsen Hesami;
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作者单位

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年(卷),期 2021(-1),-1
年度 2021
页码 -1
总页数 13
原文格式 PDF
正文语种
中图分类
关键词
Anticancer; In vitro culture; Secondary metabolite; Optimization problem; Artificial intelligence; Mathematical modeling;

机译：抗癌;体外培养;次级代谢物;优化问题;人工智能;数学建模;

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6. Forecasting and optimizing Agrobacterium-mediated genetic transformation via ensemble model- fruit fly optimization algorithm: A data mining approach using chrysanthemum databases [O] . Mohsen Hesami, Milad Alizadeh, Roohangiz Naderi, 2020

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7. A hybrid model based on general regression neural network and fruit fly optimization algorithm for forecasting and optimizing paclitaxel biosynthesis in Corylus avellana cell culture [O] . Mina Salehi, Siamak Farhadi, Ahmad Moieni, 2021

机译：基于一般回归神经网络的混合模型及果蝇预测和优化紫杉醇生物合成的植物植物植物培养综合作用

A hybrid model based on general regression neural network and fruit fly optimization algorithm for forecasting and optimizing paclitaxel biosynthesis in

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