[A study on the pattern recognition of thermophilic and mesophilic proteins].
Pattern recognition of thermophilic and mesophilic proteins were studied through principle component analysis, partial least-square regression and BP neural network. The results showed that the fitting accuracy of the three methods was 92%, 95% and 98%, respectively. And the forecasting accuracy was 60%, 72.5% and 72.5%, respectively. The best forecasting accuracy for thermophilic proteins was 75%, and for mesophilic proteins was 85%. A mathematical model was established and the biological meaning of it was expatiated on, a new method to discriminate the thermophilic and mesophilic proteins based on their sequences was established here.
Zhang
Guang-Ya
GY
Institute of Industrial Biotechnology, Huaqiao University, Quanzhou 362021, China.
Fang
Bai-Shan
BS
D000596:Amino Acids / Q000737:chemistry* / Q000378:metabolism; D016002:Discriminant Analysis; D006358:Hot Temperature; D016018:Least-Squares Analysis; D008962:Models, Theoretical; D016571:Neural Networks, Computer; D010363:Pattern Recognition, Automated; D025341:Principal Component Analysis; D011506:Proteins / Q000737:chemistry* / Q000235:genetics
D000596:Amino Acids; D011506:Proteins