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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 on global representation...
Prediction of protein–protein interaction is a difficult and an important problem in biology. In this paper, we describe a very general method for predicting protein–protein interactions. The interaction mining approach is demonstrated by building a learning system based on experimentally validated protein–protein interactions in the human gastric bacterium Helicobacter pylori. We show that combining...
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