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Predicting the subcellular localization of proteins is an important andchallenging problem. Traditional experimental approaches are often expensiveand time-consuming. Consequently, a growing number of research efforts employ aseries of machine learning approaches to predict the subcellular location ofproteins. There are two main challenges among the state-of-the-art predictionmethods. First, most of the existing techniques are designed to deal withmulti-class rather than multi-label classification, which ignores connectionsbetween multiple labels. In reality, multiple locations of particular proteinsimplies that there are vital and unique biological significances that deservespecial focus and cannot be ignored. Second, techniques for handling imbalanceddata in multi-label classification problems are necessary, but never employed.For solving these two issues, we have developed an ensemble multi-labelclassifier called HPSLPred, which can be applied for multi-label classificationwith an imbalanced protein source. For convenience, a user-friendly webserverhas been established at http://server.malab.cn/HPSLPred.

HPSLPred: An Ensemble Multi-label Classifier for Human Protein Subcellular Location Prediction with Imbalanced Source