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A Computational Method for Optimizing Experimental Environments for Phellinus igniarius via Genetic Algorithm and BP Neural Network
Joint Authors
Li, Zhongwei
Xin, Yuezhen
Wang, Xun
Sun, Beibei
Zhu, Hu
Source
Issue
Vol. 2016, Issue 2016 (31 Dec. 2016), pp.1-6, 6 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2016-08-09
Country of Publication
Egypt
No. of Pages
6
Main Subjects
Abstract EN
Flavones, the secondary metabolites of Phellinus igniarius fungus, have the properties of antioxidation and anticancer.
Because of the great medicinal value, there are large demands on flavones for medical use and research.
Flavones abstracted from natural Phellinus can not meet the medical and research need, since Phellinus in the natural environment is very rare and is hard to be cultivated artificially.
The production of flavones is mainly related to the fermentation culture of Phellinus, which made the optimization of culture conditions an important problem.
Some researches were made to optimize the fermentation culture conditions, such as the method of response surface methodology, which claimed the optimal flavones production was 1532.83 μg/mL.
In order to further optimize the fermentation culture conditions for flavones, in this work a hybrid intelligent algorithm with genetic algorithm and BP neural network is proposed.
Our method has the intelligent learning ability and can overcome the limitation of large-scale biotic experiments.
Through simulations, the optimal culture conditions are obtained and the flavones production is increased to 2200 μg/mL.
American Psychological Association (APA)
Li, Zhongwei& Sun, Beibei& Xin, Yuezhen& Wang, Xun& Zhu, Hu. 2016. A Computational Method for Optimizing Experimental Environments for Phellinus igniarius via Genetic Algorithm and BP Neural Network. BioMed Research International،Vol. 2016, no. 2016, pp.1-6.
https://search.emarefa.net/detail/BIM-1097782
Modern Language Association (MLA)
Li, Zhongwei…[et al.]. A Computational Method for Optimizing Experimental Environments for Phellinus igniarius via Genetic Algorithm and BP Neural Network. BioMed Research International No. 2016 (2016), pp.1-6.
https://search.emarefa.net/detail/BIM-1097782
American Medical Association (AMA)
Li, Zhongwei& Sun, Beibei& Xin, Yuezhen& Wang, Xun& Zhu, Hu. A Computational Method for Optimizing Experimental Environments for Phellinus igniarius via Genetic Algorithm and BP Neural Network. BioMed Research International. 2016. Vol. 2016, no. 2016, pp.1-6.
https://search.emarefa.net/detail/BIM-1097782
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1097782