Open AccessOptimization of multiplex quantitative polymerase chain reaction based on response surface methodology and an artificial neural network-genetic algorithm approach
Author(s) -
Peng Pan,
Weifeng Jin,
Xiaohong Li,
Yi Chen,
Jiahui Jiang,
Haitong Wan,
Dan Yu
Publication year - 2018
Publication title -
plos one
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.99
H-Index - 332
ISSN - 1932-6203
DOI - 10.1371/journal.pone.0200962
Subject(s) - multiplex , response surface methodology , multiplex polymerase chain reaction , correlation coefficient , mean squared error , artificial neural network , biological system , genetic algorithm , computer science , mathematics , algorithm , polymerase chain reaction , bioinformatics , statistics , biology , artificial intelligence , machine learning , genetics , gene
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 Mg 2+ 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 (R 2 ). Ultimately, optimal parameters of uni- and multiplex qPCR were determined by RSM.