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LogitBoost classifier for discriminating thermophilic and mesophilic proteins

Paper ID Volume ID Publish Year Pages File Format Full-Text
25481 43576 2007 8 PDF Available
Title
LogitBoost classifier for discriminating thermophilic and mesophilic proteins
Abstract

A novel classifier, the so-called LogitBoost classifier, was introduced to discriminate the thermophilic and mesophilic proteins according to their primary structures. When the 20-amino acid composition was chosen as the feature vector, the overall accuracy of the self-consistency check and a five-fold cross-validation procedure was 97.0% and 86.6%, respectively. To test if the method was also applicable to a wide range of biological targets, an independent testing dataset was also used. The method based on LogitBoost algorithm has achieved an overall classification accuracy of 88.9%. According to the three different validation check approaches, it was demonstrated that LogitBoost outperformed AdaBoost and performed comparably with RBF neural network and support vector machine. The influence of protein size on discrimination was addressed.

Keywords
Weka, Waikato environment for knowledge analysis; RBF neural network, radial basis function neural network; SVM, support vector machine; SE, sensitivity; SP, specificity; ACC, accuracy; MCC, Matthew's correlation coefficient; TP, true positives; FN, false
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Publisher
Database: Elsevier - ScienceDirect
Journal: Journal of Biotechnology - Volume 127, Issue 3, 10 January 2007, Pages 417–424
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