Journal
NEUROCOMPUTING
Volume 69, Issue 1-3, Pages 257-263Publisher
ELSEVIER SCIENCE BV
DOI: 10.1016/j.neucom.2005.05.007
Keywords
protein-protein interactions; machine learning
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Prediction of protein-protein interaction is a difficult and important problem in biology. Given (numerical) features, one of the existing machine learning techniques can be then applied to learn and classify proteins represented by these features. Our computational results demonstrate that a system based on K-local hyperplane outperforms the methods proposed in the literature based oil global representation of a protein pair. The approach is demonstrated by building a learning system based on experimentally validated protein-protein interactions in the human gastric bacterium Helicobacter pylori dataset and in Human dataset. (c) 2005 Elsevier B.V. All rights reserved.
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