A New Balanced Ensemble Classifier for Predicting Fungi Protein Subcellular Localization Based on Protein Primary Structures

Abstract

Protein subcellular localization provides insights into protein function. In literature, various computational methods have been developed for this problem based on protein sequences, where most methods have limited prediction accuracy. Therefore, a general computational method with high prediction accuracy is necessary. In this work, we present a novel balanced ensemble classifier for fungi protein subcellular localization prediction based only on protein sequences. We make three fold contributions to this filed. First, we present a new algorithm to cope with imbalance problem that arises in protein subcellular localization prediction, which can improve prediction accuracy significantly. Second, we employ feature selection techniques to find out most informative features for each compartment, and reduce computation cost and improve prediction accuracy at the same time. Third, an ensemble classifier combing outputs from distinct classifiers is presented to further improve prediction accuracy. The numerical results on benchmark dataset demonstrate the efficiency and effectiveness of the proposed method.