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Accurate Modeling of Complex Antitoxin Effect of Quercetin Based on Neural Networks
- Authors
- Yang, Changju; Bahar, Entaz; Yoon, Hyonok; Kim, Hyongsuk
- Issue Date
- Jan-2019
- Publisher
- World Scientific Publishing Co
- Keywords
- MTX; Quercetin; artificial neural networks; nonlinear modeling; complexity
- Citation
- International Journal of Bifurcation and Chaos in Applied Sciences and Engineering, v.29, no.1
- Indexed
- SCI
SCIE
SCOPUS
- Journal Title
- International Journal of Bifurcation and Chaos in Applied Sciences and Engineering
- Volume
- 29
- Number
- 1
- ISSN
- 0218-1274
1793-6551
- Abstract
- A nonlinear modeling of the protective effect of Quercetin (QCT) against various Mycotoxins (MTXs) has a high complexity and is conducted using artificial neural networks (ANNs). QCT is known to possess strong anti-oxidant, anti-inflammatory activity that can prevent many diseases. MTXs are highly toxic secondary metabolites that are capable of causing disease and death in humans and animals. The protective model of QCT against various MTXs (Citrinin, Patulin and Zearalenol) on HeLa cell is built accurately via learning of sparsely measured experimental data by the ANNs. It has shown that the neuro-model can predict the nonlinear protective effect of QCT against MTX-induced cytotoxicity for the measurement of percentage of inhibition of MTXs.
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