title: Supervised ensembles of prediction methods for subcellular localization.
abstract: In the past decade, many automated prediction methods for the subcellular localization of proteins have been proposed, utilizing a wide range of principles and learning approaches. Based on an experimental evaluation of different methods and their theoretical properties, we propose to combine a well-balanced set of existing approaches to new, ensemble-based prediction methods. The experimental evaluation shows that our ensembles improve substantially over the underlying base methods.
authors.affiliation: Institute for Informatics, Ludwig-Maximilians-Universität München, Oettingenstrasse 67, 80538 Munich, Germany. assfalg@dbs.ifi.lmu.de
authors.forename: Johannes
authors.initials: J
authors.lastname: Assfalg
authors.forename: Jing
authors.initials: J
authors.lastname: Gong
authors.forename: Hans-Peter
authors.initials: HP
authors.lastname: Kriegel
authors.forename: Alexey
authors.initials: A
authors.lastname: Pryakhin
authors.forename: Tiandi
authors.initials: T
authors.lastname: Wei
authors.forename: Arthur
authors.initials: A
authors.lastname: Zimek