SUPERVISED ENSEMBLES OF PREDICTION METHODS FOR SUBCELLULAR LOCALIZATION

In the past decade, many automated prediction methods for the subcellular localization of proteins have been proposed, utilizing a wide range of principles and learning approaches. Based on an experimental evaluation of different methods and on their theoretical properties, we propose to combine a well balanced set of existing approaches to new, ensemble-based prediction methods. The experimental evaluation shows our ensembles to improve substantially over the underlying base methods.