The DOI matches its Crossref record; 60 of the 60 references it cites were found in a registry or an index.
Identification of potent antimicrobial peptides via a machine-learning pipeline that mines the entire space of peptide sequences
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Cites
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Highly accurate protein structure prediction with AlphaFold
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The Perseus computational platform for comprehensive analysis of (prote)omics data
Tyanova, S., Temu, T., Sinitcyn, P., Carlson, A., Hein, M. Y., Geiger, T., Mann, M., & Cox, J. (2016) · Nature Methods · doi:10.1038/nmeth.3901 · Cited by 8,803
Accurate prediction of protein structures and interactions using a 3-track network
Baek, M., DiMaio, F., Anishchenko, I., Dauparas, J., Ovchinnikov, S., Lee, G. R., Wang, J., Cong, Q., Kinch, L. N., Schaeffer, R. D., et al. (2021) · doi:10.1101/2021.06.14.448402 · Cited by 5,549
Applications of machine learning in drug discovery and development
Vamathevan, J., Clark, D., Czodrowski, P., Dunham, I., Ferran, E., Lee, G., Li, B., Madabhushi, A., Shah, P., Spitzer, M., et al. (2019) · Nature Reviews Drug Discovery · doi:10.1038/s41573-019-0024-5 · Cited by 3,228
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Self-citation
Research works printed but not linked: 5
- Ramesh, S., Govender, T., Kruger, H. G., de la Torre, B. G. & Albericio, F. Short antimicrobial peptides (SAMPs) as a class of extraordinary promising therapeutic agents. J. Pept. Sci. 22, 438–451 (2016). · doi:10.1002/psc.2894
- Kriegler, B. & Berk, R. Stochastic gradient boosting. Comput. Stat. Data Anal. 38, 367–378 (2010).
- Mikolov, T., Sutskever, I., Chen, K., Corrado, G. S. & Dean, J. Distributed representations of words and phrases and their compositionality. Adv. Neural Inf. Process. Syst. 26, 3111–3119 (2013).
- Mohri, M., Rostamizadeh, A. & Talwalkar, A. Foundations of Machine Learning (MIT Press, 2018).
- Clarke, B. Comparing Bayes model averaging and stacking when model approximation error cannot be ignored. J. Mach. Learn. Res. 4, 683–712 (2004).
Cited by
Han, X., Liu, Q., Li, Y., Zhang, M., Liu, K., Kwok, L.-Y., Zhang, H., & Zhang, W. (2025) · Trends in Food Science & Technology · doi:10.1016/j.tifs.2025.104938 · Cited by 64
(2024) · doi:10.1002/advs.202406305 · Cited by 55
Painting Peptides With Antimicrobial Potency Through Deep Reinforcement Learning
Dong, R., Cao, Q., & Song, C. (2025) · Advanced Science · doi:10.1002/advs.202506332 · Cited by 11
Metadata
- Authors
- Huang, J., Xu, Y., Xue, Y., Huang, Y., Li, X., Chen, X., Xu, Y., Zhang, D., Zhang, P., Zhao, J., & Ji, J.
- Venue
- Nature Biomedical Engineering
- Published
- 2023-01-12
- Volume
- 7
- Issue
- 6
- Pages
- 797-810
- ISSN
- 2157-846X (ISSN-L)
- OpenAlex
- W4315784590
- Cited by
- 270
- References
- 60
- FWCI
- 39.44
- Type
- journal article
- Language
- en
- Open access
- closed
- Licence
- www.springernature.com/gp/researchers/text-and-data-mining (tdm)
- www.springernature.com/gp/researchers/text-and-data-mining (vor)
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Cite CoChat for the checks on this page, and the paper for its own findings.
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- CoChat. (2026). Citation check: Identification of potent antimicrobial peptides via a machine-learning pipeline that mines the entire space of peptide sequences. https://app.cochat.ai/papers/10.1038/s41551-022-00991-2
- The paper
- Huang, J., Xu, Y., Xue, Y., Huang, Y., Li, X., Chen, X., Xu, Y., Zhang, D., Zhang, P., Zhao, J., & Ji, J. (2023). Identification of potent antimicrobial peptides via a machine-learning pipeline that mines the entire space of peptide sequences. Nature Biomedical Engineering. https://doi.org/10.1038/s41551-022-00991-2
Data sources
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.
