Machine Learning
Ensemble classifier for protein fold pattern recognition
Bioinformatics
Data Mining: Practical Machine Learning Tools and Techniques, Second Edition (Morgan Kaufmann Series in Data Management Systems)
Bioinformatics
Boosting Methods for Protein Fold Recognition: An Empirical Comparison
BIBM '08 Proceedings of the 2008 IEEE International Conference on Bioinformatics and Biomedicine
Protein Fold Prediction Problem Using Ensemble of Classifiers
ICONIP '09 Proceedings of the 16th International Conference on Neural Information Processing: Part II
Computational Biology and Chemistry
Margin-based ensemble classifier for protein fold recognition
Expert Systems with Applications: An International Journal
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Protein Fold Recognition (PFR) is defined as assigning a given protein to a fold based on its major secondary structure. PFR is considered as an important step toward protein structure prediction and drug design. However, it still remains as an unsolved problem for biological science and bioinformatics. In this study, we explore the impact of two novel feature extraction methods namely overlapped segmented distribution and overlapped segmented autocorrelation to provide more local discriminatory information for the PFR compared to previously proposed methods found in the literature. We study the impact of our proposed feature extraction methods using 15 promising physicochemical attributes of the amino acids. Afterwards, by proposing an ensemble Support Vector Machines (SVM) which are diversely trained using features extracted from different physicochemical-based attributes, we enhance the protein fold prediction accuracy for up to 5% better than similar studies found in the literature.