ArticlePloS one2018
Optimization of multiplex quantitative polymerase chain reaction based on response surface methodology and an artificial neural network-genetic algorithm approach.
Article in PloS one, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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4 citing papers in PubMed.
- Nitrifying Communities in Biological Nitrogen Removal Processes at Tropical Municipal Wastewater Treatment Plants.Microbes and environments · 2025Article
- Fast multiplex real-time PCR assay for simultaneous detection of dog and human blood and Leishmania parasites in sand flies.Parasites & vectors · 2020Article
- Rapid and catalyst free synthesis of new bis(benzo[RSC advances · 2019Article
- Optimization of Degradation Conditions with PRG, a Polysaccharide fromMolecules (Basel, Switzerland) · 2019Article
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7 authors.
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Abstract
Multiplex quantitative polymerase chain reaction (qPCR) has found an increasing range of applications. The construction of a reliable and dynamic mathematical model for multiplex qPCR that analyzes the effects of interactions between variables is therefore especially important. This work aimed to analyze the effects of interactions between variables through response surface method (RSM) for uni- and multiplex qPCR, and further optimize the parameters by constructing two mathematical models via RSM and back-propagation neural network-genetic algorithm (BPNN-GA) respectively. The statistical analysis showed that Mg2+ was the most important factor for both uni- and multiplex qPCR. Dynamic models of uni- and multiplex qPCR could be constructed using both RSM and BPNN-GA methods. But RSM was better than BPNN-GA on prediction performance in terms of the mean absolute error (MAE), the mean square error (MSE) and the Coefficient of Determination (R2). Ultimately, optimal parameters of uni- and multiplex qPCR were determined by RSM.
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