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Discrimination of thermophilic and mesophilic proteins via pattern recognition methods

Paper ID Volume ID Publish Year Pages File Format Full-Text
36372 45131 2006 5 PDF Available
Title
Discrimination of thermophilic and mesophilic proteins via pattern recognition methods
Abstract

Four pattern recognition methods, namely, principal component analysis (PCA), stepwise regression (SR), partial least-square regression (PLSR), and backpropagation neural network, were used to discriminate thermophilic and mesophilic proteins. And four models were made to classify between these two kinds of proteins. To some degree the prediction accuracy of the methods was encouraging except for principal component analysis. Results showed that the average fitting accuracy of the four methods was 92%, 96%, 95% and 98%, respectively. And the average prediction reliability was 60%, 67.5%, 72.5% and 72.5%, respectively, the best prediction reliability for thermophilic proteins was 75%, and for mesophilic proteins was 85%.

Keywords
PCA, principal component analysis; SR, stepwise regression; PLSR, partial least-square regression; PC-ANN, principal component artificial neural networks; DPS, data processing system; LVs, latent variables; CPU, central processing unit; PCs, principal com
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Publisher
Database: Elsevier - ScienceDirect
Journal: Process Biochemistry - Volume 41, Issue 3, March 2006, Pages 552–556
Authors
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Subjects
Physical Sciences and Engineering Chemical Engineering Bioengineering
Get Full-Text Now
Don't Miss Today's Special Offer
Price was $35.95
You save - $31
Price after discount Only $4.95
100% Money Back Guarantee
Full-text PDF Download
Online Support
Any Questions? feel free to contact us