Name
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. |
Year | Venue | Keywords |
|---|---|---|
2006 | Pacific Symposium on Biocomputing | protein sequence,system performance,system integration |
Field | DocType | ISSN |
Small number,Data set,Protein sequencing,Biology,Bioinformatics,Subcellular localization | Conference | 2335-6936 |
Citations | PageRank | References |
11 | 0.77 | 14 |
Authors | ||
8 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Annette Höglund | 1 | 105 | 5.38 |
| Torsten Blum | 2 | 231 | 12.70 |
| Scott Brady | 3 | 72 | 3.88 |
| Pierre Dönnes | 4 | 206 | 14.93 |
| John San Miguel | 5 | 13 | 2.84 |
| Matthew Rocheford | 6 | 11 | 0.77 |
| Oliver Kohlbacher | 7 | 975 | 101.91 |
| Hagit Shatkay | 8 | 910 | 96.13 |