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Abdollah Dehzangi , Griffith University, and National ICT Australia (NICTA), Brisbane
Kuldip Paliwal , Griffith University, Brisbane
Alok Sharma , Griffith University, Brisbane and University of the South Pacific, Fiji
Omid Dehzangi , University of Texas at Dallas, Dallas
Abdul Sattar , Griffith University, and National ICT Australia (NICTA), Brisbane
ABSTRACT
Better understanding of structural class of a given protein reveals important information about its overall folding type and its domain. It can also be directly used to provide critical information on general tertiary structure of a protein which has a profound impact on protein function determination and drug design. Despite tremendous enhancements made by pattern recognition-based approaches to solve this problem, it still remains as an unsolved issue for bioinformatics which demands more attention and exploration. In this study, we propose a novel feature extraction model which incorporates physicochemical and evolutionary-based information simultaneously. We also propose overlapped segmented distribution and autocorrelation based feature extraction methods to provide more local and global discriminatory information. The proposed feature extraction methods are explored for 15 most promising attributes that are selected from a wide range of physicochemical-based attributes. Finally, by applying an ensemble of different classifiers namely, Adaboost.M1, LogitBoost, Naive Bayes, Multi-Layer Perceptron (MLP), and Support Vector Machine (SVM) we show enhancement of the protein structural class prediction accuracy for four popular benchmarks.
INDEX TERMS
Physicochemical-based features, Bioinformatics, Computational Biology, Protein Structural class prediction problem, Mixture of feature extraction models, Overlapped segmented distribution, Overlapped segmented autocorrelation, Ensemble of different classifiers
CITATION
Abdollah Dehzangi, Kuldip Paliwal, Alok Sharma, Omid Dehzangi, Abdul Sattar, "A Combination of Feature Extraction Methods with an Ensemble of Different Classifiers for Protein Structural Class Prediction Problem", IEEE/ACM Transactions on Computational Biology and Bioinformatics, , no. 1, pp. 1, PrePrints PrePrints, doi:10.1109/TCBB.2013.65
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