title: Protein (multi-)location prediction: utilizing interdependencies via a generative model.
abstract: BACKGROUND
  Proteins are responsible for a multitude of vital tasks in all living organisms. Given that a protein's function and role are strongly related to its subcellular location, protein location prediction is an important research area. While proteins move from one location to another and can localize to multiple locations, most existing location prediction systems assign only a single location per protein. A few recent systems attempt to predict multiple locations for proteins, however, their performance leaves much room for improvement. Moreover, such systems do not capture dependencies among locations and usually consider locations as independent. We hypothesize that a multi-location predictor that captures location inter-dependencies can improve location predictions for proteins.
  
  
  RESULTS
  We introduce a probabilistic generative model for protein localization, and develop a system based on it-which we call MDLoc-that utilizes inter-dependencies among locations to predict multiple locations for proteins. The model captures location inter-dependencies using Bayesian networks and represents dependency between features and locations using a mixture model. We use iterative processes for learning model parameters and for estimating protein locations. We evaluate our classifier MDLoc, on a dataset of single- and multi-localized proteins derived from the DBMLoc dataset, which is the most comprehensive protein multi-localization dataset currently available. Our results, obtained by using MDLoc, significantly improve upon results obtained by an initial simpler classifier, as well as on results reported by other top systems.
  
  
  METHODS
  MDLoc is available at: http://www.eecis.udel.edu/∼compbio/mdloc.
authors.affiliation: Department of Computer and Information Sciences, University of Delaware, Newark, DE, USA, Applied Bioinformatics, Center for Bioinformatics, University of Tuebingen, Germany, Center for Bioinformatics and Computational Biology, University of Delaware, Newark, DE, USA and School of Computing, Queen's University, Kingston, ON, Canada.
authors.forename: Ramanuja
authors.initials: R
authors.lastname: Simha
authors.affiliation: Department of Computer and Information Sciences, University of Delaware, Newark, DE, USA, Applied Bioinformatics, Center for Bioinformatics, University of Tuebingen, Germany, Center for Bioinformatics and Computational Biology, University of Delaware, Newark, DE, USA and School of Computing, Queen's University, Kingston, ON, Canada.
authors.forename: Sebastian
authors.initials: S
authors.lastname: Briesemeister
authors.affiliation: Department of Computer and Information Sciences, University of Delaware, Newark, DE, USA, Applied Bioinformatics, Center for Bioinformatics, University of Tuebingen, Germany, Center for Bioinformatics and Computational Biology, University of Delaware, Newark, DE, USA and School of Computing, Queen's University, Kingston, ON, Canada.
authors.forename: Oliver
authors.initials: O
authors.lastname: Kohlbacher
authors.affiliation: Department of Computer and Information Sciences, University of Delaware, Newark, DE, USA, Applied Bioinformatics, Center for Bioinformatics, University of Tuebingen, Germany, Center for Bioinformatics and Computational Biology, University of Delaware, Newark, DE, USA and School of Computing, Queen's University, Kingston, ON, Canada Department of Computer and Information Sciences, University of Delaware, Newark, DE, USA, Applied Bioinformatics, Center for Bioinformatics, University of Tuebingen, Germany, Center for Bioinformatics and Computational Biology, University of Delaware, Newark, DE, USA and School of Computing, Queen's University, Kingston, ON, Canada Department of Computer and Information Sciences, University of Delaware, Newark, DE, USA, Applied Bioinformatics, Center for Bioinformatics, University of Tuebingen, Germany, Center for Bioinformatics and Computational Biology, University of Delaware, Newark, DE, USA and School of Computing, Queen's University, Kingston, ON, Canada.
authors.forename: Hagit
authors.initials: H
authors.lastname: Shatkay
mesh_terms: D001499:Bayes Theorem; D030562:Databases, Protein; D006801:Humans; D008962:Models, Theoretical; D021381:Protein Transport; D011506:Proteins / Q000378:metabolism*; D013347:Subcellular Fractions
chemical_list: D011506:Proteins