Information Fusion for Biological Prediction

Information fusion has become a powerful tool for challenging applications such as biological prediction problems. In this paper, we apply a new information-theoretical fusion technique to HIV-1 protease cleavage site prediction, which is a problem that has been in the focus of much interest and investigation of the machine learning community recently. It poses a difficult classification task due to its high dimensional feature space and a relatively small set of available training patterns. We also apply a new set of biophysical features to this problem and present experiments with neural networks, support vector machines, and decision trees. Application of our feature set results in high recognition rates and concise decision trees, producing manageable rule sets that can guide future experiments. In particular, we found a combination of neural networks and support vector machines to be beneficial for this problem.