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A uniform design‐based back propagation neural network model for amino acid composition and optimal pH in G/11 xylanase
Author(s) -
Zhang Guangya,
Fang Baishan
Publication year - 2006
Publication title -
journal of chemical technology and biotechnology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.64
H-Index - 117
eISSN - 1097-4660
pISSN - 0268-2575
DOI - 10.1002/jctb.1510
Subject(s) - xylanase , artificial neural network , backpropagation , mean squared error , mathematics , composition (language) , approximation error , biological system , unit (ring theory) , mean absolute error , chemistry , regression , mean absolute percentage error , statistics , chromatography , computer science , biochemistry , artificial intelligence , biology , enzyme , linguistics , philosophy , mathematics education
A link between amino acid composition and optimal pH in G/11 xylanase was established. A back propagation neural network (BPNN) was used as the mathematical tool and a uniform design method was employed to optimise the architecture of the BPNN. Results showed that the calculated and predicted pHs fitted the optimal pHs of xylanase very well, with mean absolute percentage errors (MAPEs) of 3.02 and 4.06%, mean square errors (MSEs) of 0.19 and 0.19 pH unit and mean absolute errors (MAEs) of 0.11 and 0.19 pH unit respectively. The new model performed better in fitting and prediction compared with a previously reported model based on stepwise regression. Copyright © 2006 Society of Chemical Industry