The DOI matches its Crossref record; 60 of the 60 references it cites were found in a registry or an index.
De novo peptide sequencing by deep learning
Open accessAbstract
Significance: Our method, DeepNovo, introduces deep learning to de novo peptide sequencing from tandem MS data, the key technology for protein characterization in proteomics research. DeepNovo achieves major improvement of sequencing accuracy over state of the art methods and subsequently enables complete assembly of protein sequences without assisting databases. Our model is retrainable to adapt to any sources of data and provides a complete end-to-end training and prediction solution, an important feature given the growing massive amount of data. Our study also presents an innovative approach to combine deep learning and dynamic programming to solve optimization problems.
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Artificial intelligence-based multi-omics analysis fuels cancer precision medicine
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Bioinformatics Methods for Mass Spectrometry-Based Proteomics Data Analysis
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Ancient Biomolecules and Evolutionary Inference
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Metadata
- Authors
- Tran, N. H., Zhang, X., Xin, L., Shan, B., & Li, M.
- Venue
- Proceedings of the National Academy of Sciences
- Published
- 2017-07-18
- Volume
- 114
- Issue
- 31
- Pages
- 8247-8252
- ISSN
- 0027-8424 (ISSN-L)
- 1091-6490
- OpenAlex
- W2736859409
- Cited by
- 441
- References
- 60
- FWCI
- 13.82
- Type
- journal article
- Language
- en
- Open access
- green
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- Tran, N. H., Zhang, X., Xin, L., Shan, B., & Li, M. (2017). De novo peptide sequencing by deep learning. Proceedings of the National Academy of Sciences. https://doi.org/10.1073/pnas.1705691114
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
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