Significantly improved prediction of subcellular localization by integrating text and protein sequence data.
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.
Höglund
Annette
A
Div. for Simulation of Biological Systems, ZBIT/WSI, University of Tübingen, Sand 14, D-72076 Tübingen, Germany.
Blum
Torsten
T
Brady
Scott
S
Dönnes
Pierre
P
Miguel
John San
JS
Rocheford
Matthew
M
Kohlbacher
Oliver
O
Shatkay
Hagit
H
D001185:Artificial Intelligence; D003198:Computer Simulation; D030562:Databases, Protein; D008954:Models, Biological; D011506:Proteins / Q000235:genetics* / Q000378:metabolism*; D013347:Subcellular Fractions / Q000378:metabolism*
D011506:Proteins