The DOI matches its Crossref record; 9 of the 9 references it cites were found in a registry or an index.
UDSMProt: universal deep sequence models for protein classification
Open accessAbstract
Motivation: Inferring the properties of a protein from its amino acid sequence is one of the key problems in bioinformatics. Most state-of-the-art approaches for protein classification are tailored to single classification tasks and rely on handcrafted features, such as position-specific-scoring matrices from expensive database searches. We argue that this level of performance can be reached or even be surpassed by learning a task-agnostic representation once, using self-supervised language modeling, and transferring it to specific tasks by a simple fine-tuning step. Results: We put forward a universal deep sequence model that is pre-trained on unlabeled protein sequences from Swiss-Prot and fine-tuned on protein classification tasks. We apply it to three prototypical tasks, namely enzyme class prediction, gene ontology prediction and remote homology and fold detection. The proposed method performs on par with state-of-the-art algorithms that were tailored to these specific tasks or, for two out of three tasks, even outperforms them. These results stress the possibility of inferring protein properties from the sequence alone and, on more general grounds, the prospects of modern natural language processing methods in omics. Moreover, we illustrate the prospects for explainable machine learning methods in this field by selected case studies. Availability and implementation: Source code is available under https://github.com/nstrodt/UDSMProt. Supplementary information: Supplementary data are available at Bioinformatics online.
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Cites
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UniProt: a worldwide hub of protein knowledge
The UniProt Consortium (2019) · Nucleic Acids Research · doi:10.1093/nar/gky1049 · Cited by 7,698
The Pfam protein families database in 2019
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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
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Using deep learning to annotate the protein universe
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Cited by
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
ProteinBERT: a universal deep-learning model of protein sequence and function
Brandes, N., Ofer, D., Peleg, Y., Rappoport, N., & Linial, M. (2022) · Bioinformatics · doi:10.1093/bioinformatics/btac020 · Cited by 1,009
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
Protein design and variant prediction using autoregressive generative models
Shin, J.-E., Riesselman, A. J., Kollasch, A. W., McMahon, C., Simon, E., Sander, C., Manglik, A., Kruse, A. C., & Marks, D. S. (2021) · Nature Communications · doi:10.1038/s41467-021-22732-w · Cited by 320
Frozen Pretrained Transformers as Universal Computation Engines
Lu, K., Grover, A., Abbeel, P., & Mordatch, I. (2022) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v36i7.20729 · Cited by 113
“2019), and UDSMProt for protein sequences (Strodthoff et al. 2020).”
Background
Metadata
- Authors
- Strodthoff, N., Wagner, P., Wenzel, M., & Samek, W.
- Venue
- Bioinformatics
- Publisher
- Oxford University Press (OUP)
- Published
- 2020-01-08
- Volume
- 36
- Issue
- 8
- Pages
- 2401-2409
- ISSN
- 1367-4803 (ISSN-L)
- 1367-4811
- OpenAlex
- W2999481648
- Cited by
- 189
- References
- 9
- FWCI
- 7.74
- Type
- journal article
- Language
- en
- Open access
- hybrid
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