New Feature Weighting Technique for Predicting Protein Subcellular Localization

Protein subcellular localization prediction is an important task with significant applications including the discovery of molecular functions of proteins. A number of prediction techniques have been developed in the past years based on protein sequence information. In this paper, we propose a new technique for predicting subcellular localizations of proteins using improved features of proteins extracted from protein sequences. The method is effective in inducing the features from protein sequences in multiple localizations. We evaluated the method using six datasets of proteins from bacteria, gram-negative and gram-positive, plant, and non-plant proteins and compared the results with recent methods. The evaluation results with six protein localization datasets showed that the method is promising and competitive for predicting protein localizations. This method is fairly effective in extracting strong features from protein sequences which will have significant impact on great deal of research work that relies mainly on protein sequence information for discovering molecular functions of proteins, drug design, and disease-protein associations.