title: [A model for amino acid composition and optimum pH in G/11 xylanase based on neural networks].
abstract: In this paper, a prediction model for amino acid composition and optimum pH of xylanase in G/11 family was established in terms of an artificial neural networks based on uniform design. Results showed that the calculated and predicted pHs fitted the optimum pHs of xylanase very well and the MAPEs (Mean mean Absolute Percent Error) were 3.02% and 4.06%, the MSEs (Mean Square Error) were 0.19 and 0.19 pH unit, the MAE (Mean Absolute Error) were 0.11 and 0.19 pH unit, respectively. It was better in fittings and predictions compared with the reported model based on stepwise regression.
authors.affiliation: Institute of Industrial Biotechnology, Huaqiao University, Quanzhou 362021, China.
authors.forename: Guang-Ya
authors.initials: GY
authors.lastname: Zhang
authors.forename: Bai-Shan
authors.initials: BS
authors.lastname: Fang
mesh_terms: D000596:Amino Acids / Q000737:chemistry*; D006863:Hydrogen-Ion Concentration; D008956:Models, Chemical; D016571:Neural Networks, Computer; D043325:Xylan Endo-1,3-beta-Xylosidase / Q000737:chemistry* / Q000302:isolation & purification
chemical_list: D000596:Amino Acids; D043325:Xylan Endo-1,3-beta-Xylosidase