A novel ensemble of classifiers for protein fold recognition
Predicting the three-dimensional structure of a protein from its amino acid sequence is an important problem in bioinformatics and a challenging task for machine-learning algorithms. We propose a new ensemble of K-local hyperplane based on random subspace and feature selection, and tested it on a real-world dataset containing 27 SCOP folds from [C. Ding, I. Dubchak, Multi-class protein fold recognition using support vector machines and neural networks, Bioinformatics, 17(4) (2001) 349358.].
protein fold recognition random subspace feature selection