  Predicting the subcellular localization of proteins is an important and
challenging problem. Traditional experimental approaches are often expensive
and time-consuming. Consequently, a growing number of research efforts employ a
series of machine learning approaches to predict the subcellular location of
proteins. There are two main challenges among the state-of-the-art prediction
methods. First, most of the existing techniques are designed to deal with
multi-class rather than multi-label classification, which ignores connections
between multiple labels. In reality, multiple locations of particular proteins
implies that there are vital and unique biological significances that deserve
special focus and cannot be ignored. Second, techniques for handling imbalanced
data in multi-label classification problems are necessary, but never employed.
For solving these two issues, we have developed an ensemble multi-label
classifier called HPSLPred, which can be applied for multi-label classification
with an imbalanced protein source. For convenience, a user-friendly webserver
has been established at http://server.malab.cn/HPSLPred.
