title: Significantly improved prediction of subcellular localization by integrating text and protein sequence data.
abstract: Computational prediction of protein subcellular localization is a challenging problem. Several approaches have been presented during the past few years; some attempt to cover a wide variety of localizations, while others focus on a small number of localizations and on specific organisms. We present a comprehensive system, integrating protein sequence-derived data and text-based information. Itis tested on three large data sets, previously used by leading prediction methods. The results demonstrate that our system performs significantly better than previously reported results, for a wide range of eukaryotic subcellular localizations.
authors.affiliation: Div. for Simulation of Biological Systems, ZBIT/WSI, University of Tübingen, Sand 14, D-72076 Tübingen, Germany.
authors.forename: Annette
authors.initials: A
authors.lastname: Höglund
authors.forename: Torsten
authors.initials: T
authors.lastname: Blum
authors.forename: Scott
authors.initials: S
authors.lastname: Brady
authors.forename: Pierre
authors.initials: P
authors.lastname: Dönnes
authors.forename: John San
authors.initials: JS
authors.lastname: Miguel
authors.forename: Matthew
authors.initials: M
authors.lastname: Rocheford
authors.forename: Oliver
authors.initials: O
authors.lastname: Kohlbacher
authors.forename: Hagit
authors.initials: H
authors.lastname: Shatkay
mesh_terms: D001185:Artificial Intelligence; D003198:Computer Simulation; D030562:Databases, Protein; D008954:Models, Biological; D011506:Proteins / Q000235:genetics* / Q000378:metabolism*; D013347:Subcellular Fractions / Q000378:metabolism*
chemical_list: D011506:Proteins