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Knowing the location of a protein within the cell is important forunderstanding its function, role in biological processes, and potential use asa drug target. Much progress has been made in developing computational methodsthat predict single locations for proteins, assuming that proteins localize toa single location. However, it has been shown that proteins localize tomultiple locations. While a few recent systems have attempted to predictmultiple locations of proteins, they typically treat locations as independentor capture inter-dependencies by treating each locations-combination present inthe training set as an individual location-class. We present a new method and apreliminary system we have developed that directly incorporatesinter-dependencies among locations into the multiple-location-predictionprocess, using a collection of Bayesian network classifiers. We evaluate oursystem on a dataset of single- and multi-localized proteins. Our results,obtained by incorporating inter-dependencies are significantly higher thanthose obtained by classifiers that do not use inter-dependencies. Theperformance of our system on multi-localized proteins is comparable to a topperforming system (YLoc+), without restricting predictions to be based only onlocation-combinations present in the training set.

Protein (Multi-)Location Prediction: Using Location Inter-Dependencies in a Probabilistic Framework