Article ID Journal Published Year Pages File Type
411029 Neurocomputing 2006 4 Pages PDF
Abstract

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) 349–358.].

Related Topics
Physical Sciences and Engineering Computer Science Artificial Intelligence
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