The DOI matches its OpenAlex record; 6 of the 6 references it cites were found in a registry or an index.
SignalP 6.0 predicts all five types of signal peptides using protein language models
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Citations
Cites
Needleman, S. B., & Wunsch, C. D. (1970) · Journal of Molecular Biology · doi:10.1016/0022-2836(70)90057-4 · Cited by 11,288
Comparison of the predicted and observed secondary structure of T4 phage lysozyme
Matthews, B. W. (1975) · Biochimica et Biophysica Acta (BBA) - Protein Structure · doi:10.1016/0005-2795(75)90109-9 · Cited by 5,399
Rives, A., Meier, J., Sercu, T., Goyal, S., Lin, Z., Liu, J., Guo, D., Ott, M., Zitnick, C. L., Ma, J., et al. (2021) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.2016239118 · Cited by 3,286
Rives, A., Meier, J., Sercu, T., Goyal, S., Lin, Z., Liu, J., Guo, D., Ott, M., Zitnick, C. L., Ma, J., et al. (2019) · doi:10.1101/622803 · Cited by 3,280
ProtTrans: Toward Understanding the Language of Life Through Self-Supervised Learning
Elnaggar, A., Heinzinger, M., Dallago, C., Rehawi, G., Wang, Y., Jones, L., Gibbs, T., Feher, T., Angerer, C., Steinegger, M., et al. (2022) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2021.3095381 · Cited by 2,329
Unified rational protein engineering with sequence-based deep representation learning
Alley, E. C., Khimulya, G., Biswas, S., AlQuraishi, M., & Church, G. M. (2019) · Nature Methods · doi:10.1038/s41592-019-0598-1 · Cited by 1,131
Cited by
Bilingual language model for protein sequence and structure
Heinzinger, M., Weissenow, K., Sanchez, J. G., Henkel, A., Mirdita, M., Steinegger, M., & Rost, B. (2024) · NAR Genomics and Bioinformatics · doi:10.1093/nargab/lqae150 · Cited by 311
DeepLoc 2.1: multi-label membrane protein type prediction using protein language models
Ødum, M. T., Teufel, F., Thumuluri, V., Almagro Armenteros, J. J., Johansen, A. R., Winther, O., & Nielsen, H. (2024) · Nucleic Acids Research · doi:10.1093/nar/gkae237 · Cited by 285
Self-citationFine-tuning protein language models boosts predictions across diverse tasks
Schmirler, R., Heinzinger, M., & Rost, B. (2024) · Nature Communications · doi:10.1038/s41467-024-51844-2 · Cited by 229
Novel machine learning approaches revolutionize protein knowledge
Bordin, N., Dallago, C., Heinzinger, M., Kim, S., Littmann, M., Rauer, C., Steinegger, M., Rost, B., & Orengo, C. (2023) · Trends in Biochemical Sciences · doi:10.1016/j.tibs.2022.11.001 · Cited by 72
Sugihara, Y., Abe, Y., Takagi, H., Abe, A., Shimizu, M., Ito, K., Kanzaki, E., Oikawa, K., Kourelis, J., Langner, T., et al. (2023) · PLOS Biology · doi:10.1371/journal.pbio.3001945 · Cited by 59
Metadata
- Authors
- Teufel, F., Armenteros, J. J. A., Johansen, A. R., Gíslason, M. H., Pihl, S. I., Tsirigos, K. D., Winther, O., Brunak, S., von HEIJNE, G., & Nielsen, H.
- Venue
- Repository for Publications and Research Data (ETH Zurich)
- Publisher
- ETH Zurich
- Published
- 2022-07-01
- OpenAlex
- W4206950245
- Cited by
- 1,905
- References
- 6
- FWCI
- 131.13
- Type
- journal article
- Language
- en
- Open access
- green
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- CoChat. (2026). Citation check: SignalP 6.0 predicts all five types of signal peptides using protein language models. https://app.cochat.ai/papers/10.3929/ethz-b-000524414
- The paper
- Teufel, F., Armenteros, J. J. A., Johansen, A. R., Gíslason, M. H., Pihl, S. I., Tsirigos, K. D., Winther, O., Brunak, S., von HEIJNE, G., & Nielsen, H. (2022). SignalP 6.0 predicts all five types of signal peptides using protein language models. Repository for Publications and Research Data (ETH Zurich). https://doi.org/10.3929/ethz-b-000524414
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Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
