Name
Abstract | ||
|---|---|---|
Motivation: The identification of signal peptides in protein sequences is an important step toward protein localization and function characterization. Results: Here, we present DeepSig, an improved approach for signal peptide detection and cleavage-site prediction based on deep learning methods. Comparative benchmarks performed on an updated independent dataset of proteins show that DeepSig is the current best performing method, scoring better than other available state-of-the-art approaches on both signal peptide detection and precise cleavage-site identification. |
Year | DOI | Venue |
|---|---|---|
2018 | 10.1093/bioinformatics/btx818 | BIOINFORMATICS |
Field | DocType | Volume |
Data mining,Standalone program,Computer science,Protein subcellular localization prediction,Artificial intelligence,Signal peptide,Deep learning,Web server | Journal | 34 |
Issue | ISSN | Citations |
10 | 1367-4803 | 4 |
PageRank | References | Authors |
0.39 | 12 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Castrense Savojardo | 1 | 99 | 10.27 |
| Pier Luigi Martelli | 2 | 375 | 29.49 |
| Piero Fariselli | 3 | 851 | 96.03 |
| Rita Casadio | 4 | 1032 | 108.10 |