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Significantly improved prediction of subcellular localization by integrating text and protein sequence data.

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  • 1Div. for Simulation of Biological Systems, ZBIT/WSI, University of Tübingen, Sand 14, D-72076 Tübingen, Germany.


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.

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