Protein Fold Prediction Problem Using Ensemble of Classifiers
ICONIP '09 Proceedings of the 16th International Conference on Neural Information Processing: Part II
ACIIDS'11 Proceedings of the Third international conference on Intelligent information and database systems - Volume Part I
Using rotation forest for protein fold prediction problem: an empirical study
EvoBIO'10 Proceedings of the 8th European conference on Evolutionary Computation, Machine Learning and Data Mining in Bioinformatics
Ensemble of diversely trained support vector machines for protein fold recognition
ACIIDS'13 Proceedings of the 5th Asian conference on Intelligent Information and Database Systems - Volume Part I
Protein fold recognition using segmentation-based feature extraction model
ACIIDS'13 Proceedings of the 5th Asian conference on Intelligent Information and Database Systems - Volume Part I
Protein fold recognition with a two-layer method based on SVM-SA, WP-NN and C4.5 TLM-SNC
International Journal of Data Mining and Bioinformatics
Comparing ensemble learning methods based on decision tree classifiers for protein fold recognition
International Journal of Data Mining and Bioinformatics
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Protein fold recognition is the prediction of protein's tertiary structure (Fold) given the protein's sequence without relying on sequence similarity. Using machine learning techniques for protein fold recognition, most of the state-of-the-art research has focused on more traditional algorithms such as Support Vector Machines (SVM), K-Nearest Neighbor (KNN) and Neural Networks (NN). In this paper, we present an empirical study of two variants of Boosting algorithms - AdaBoost and LogitBoost for the problem of fold recognition. Prediction accuracy is measured on a dataset with proteins from 27 most populated folds from the SCOP database, and is compared with results from other literature using SVM, KNN and NN algorithms on the same dataset. Overall, Boosting methods achieve 60\%\ fold recognition accuracy on an independent test protein dataset which is the highest prediction achieved when compared with the accuracy values obtained with other methods proposed in the literature. Boosting algorithms have the potential to build efficient classification models in a very fast manner.