Random forest for classification of thermophilic and psychrophilic proteins based on amino acid composition distribution / 生物工程学报
Chinese Journal of Biotechnology
;
(12): 302-308, 2008.
Article
in Chinese
| WPRIM
| ID: wpr-276123
ABSTRACT
We used amino acid composition distribution (AACD) to discriminate thermophilic and psychrophilic proteins. We used 10-fold cross-validation and independent testing with other dataset to evaluate the models. The results showed that when the segment was 1, the overall accuracy reached 92.9% and 90.2%, respectively. The AACD method improved the prediction accuracy when support vector machine was used as the classifier. When six new features were introduced, the overall accuracy of random forest improved to 93.2% and 92.2%, the areas under the receiver operation characteristic curve were 0.9771 and 0.9696, which was better than other ensemble classifiers and comparable with that of SVM.
Full text:
Available
Index:
WPRIM (Western Pacific)
Main subject:
Temperature
/
Bacteria
/
Bacterial Proteins
/
Algorithms
/
Computer Simulation
/
Models, Molecular
/
Discriminant Analysis
/
Chemistry
/
Amino Acid Sequence
/
Classification
Type of study:
Controlled clinical trial
/
Prognostic study
Language:
Chinese
Journal:
Chinese Journal of Biotechnology
Year:
2008
Type:
Article
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