Significantly Improved Prediction of Subcellular Localization by Integrating Text and Protein Sequence DataHoglund A, Blum T, Brady S, Donnes P, San Miguel J, Rocheford M, Kohlbacher O, Shatkay H |
|
AbstractComputational 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. It is 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. | |
[Full-Text PDF] [PSB Home Page] |