The DOI matches its Crossref record; 7 of the 7 references it cites were found in a registry or an index.
DeepSig: deep learning improves signal peptide detection in proteins
Open accessAbstract
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. Availability and implementation: DeepSig is available as both standalone program and web server at https://deepsig.biocomp.unibo.it. All datasets used in this study can be obtained from the same website. Supplementary information: Supplementary data are available at Bioinformatics online.
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ImageNet classification with deep convolutional neural networks
Krizhevsky, A., Sutskever, I., & Hinton, G. E. (2017) · Communications of the ACM · doi:10.1145/3065386 · Cited by 112,155
LeCun, Y., Bengio, Y., & Hinton, G. (2015) · Nature · doi:10.1038/nature14539 · Cited by 84,126
SignalP 4.0: discriminating signal peptides from transmembrane regions
Petersen, T. N., Brunak, S., von Heijne, G., & Nielsen, H. (2011) · Nature Methods · doi:10.1038/nmeth.1701 · Cited by 8,847
Intriguing properties of neural networks
Szegedy, C., Zaremba, W., Sutskever, I., Bruna, J., Erhan, D., Goodfellow, I., & Fergus, R. (2013) · arXiv · doi:10.48550/arxiv.1312.6199 · Cited by 8,143
Deep Inside Convolutional Networks: Visualising Image Classification Models and Saliency Maps
Simonyan, K., Vedaldi, A., & Zisserman, A. (2013) · arXiv · doi:10.48550/arxiv.1312.6034 · Cited by 5,370
On Pixel-Wise Explanations for Non-Linear Classifier Decisions by Layer-Wise Relevance Propagation
Bach, S., Binder, A., Montavon, G., Klauschen, F., Müller, K.-R., & Samek, W. (2015) · PLOS ONE · doi:10.1371/journal.pone.0130140 · Cited by 4,524
Explaining nonlinear classification decisions with deep taylor decomposition
Montavon, G., Lapuschkin, S., Binder, A., Samek, W., & Müller, K.-R. (2017) · Pattern Recognition · doi:10.1016/j.patcog.2016.11.008 · Cited by 1,357
Cited by
BUSCA: an integrative web server to predict subcellular localization of proteins
Savojardo, C., Martelli, P. L., Fariselli, P., Profiti, G., & Casadio, R. (2018) · Nucleic Acids Research · doi:10.1093/nar/gky320 · Cited by 452
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Metadata
- Authors
- Savojardo, C., Martelli, P. L., Fariselli, P., & Casadio, R.
- Venue
- Bioinformatics
- Publisher
- Oxford University Press (OUP)
- Published
- 2017-12-21
- Volume
- 34
- Issue
- 10
- Pages
- 1690-1696
- ISSN
- 1367-4803 (ISSN-L)
- 1367-4811
- OpenAlex
- W2776525063
- Cited by
- 160
- References
- 7
- FWCI
- 2.62
- Type
- journal article
- Language
- en
- Open access
- hybrid
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- Savojardo, C., Martelli, P. L., Fariselli, P., & Casadio, R. (2018). DeepSig: deep learning improves signal peptide detection in proteins. Bioinformatics. https://doi.org/10.1093/bioinformatics/btx818
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