Predicting subcellular localization of proteins using machine-learned classifiers

Zhiyong Lu, Duane Szafron, Russell Greiner, Paul Lu, David S. Wishart, Brett Poulin, John Anvik, Cam Macdonell, Roman Eisner

Abstract: Identifying the destination or localization of proteins is key to understanding their function and facilitating their purification. A number of existing computational prediction methods are based on sequence analysis. However, these methods are limited in scope, accuracy and most particularly breadth of coverage. Rather than using sequence information alone, we have explored the use of database text annotations from homologs and machine learning to substantially improve the prediction of subcellular location.