LMPhosSite: A Deep Learning-Based Approach for General Protein Phosphorylation Site Prediction Using Embeddings from the Local Window Sequence and Pretrained Protein Language Model
- DOI
- 10.1021/acs.jproteome.2c00667
- Published
- 2023-07-17
- Container
- Journal of Proteome Research
- Publisher
- American Chemical Society (ACS)
- Open access
- unknown
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Cite this work
BibTeX
@article{allodium:10.1021/acs.jproteome.2c00667,
title = {LMPhosSite: A Deep Learning-Based Approach for General Protein Phosphorylation Site Prediction Using Embeddings from the Local Window Sequence and Pretrained Protein Language Model},
author = {Subash C. Pakhrin and Suresh Pokharel and Pawel Pratyush and Meenal Chaudhari and Hamid D. Ismail and Dukka B. KC},
year = {2023},
journal = {Journal of Proteome Research},
doi = {10.1021/acs.jproteome.2c00667},
url = {https://doi.org/10.1021/acs.jproteome.2c00667}
}RIS
TY - JOUR TI - LMPhosSite: A Deep Learning-Based Approach for General Protein Phosphorylation Site Prediction Using Embeddings from the Local Window Sequence and Pretrained Protein Language Model AU - Subash C. Pakhrin AU - Suresh Pokharel AU - Pawel Pratyush AU - Meenal Chaudhari AU - Hamid D. Ismail AU - Dukka B. KC PY - 2023 JO - Journal of Proteome Research DO - 10.1021/acs.jproteome.2c00667 UR - https://doi.org/10.1021/acs.jproteome.2c00667 ER -
APA
Pakhrin, S. C., Pokharel, S., Pratyush, P., Chaudhari, M., Ismail, H. D., & KC, D. B. (2023). LMPhosSite: A Deep Learning-Based Approach for General Protein Phosphorylation Site Prediction Using Embeddings from the Local Window Sequence and Pretrained Protein Language Model. Journal of Proteome Research. https://doi.org/10.1021/acs.jproteome.2c00667
Source records
- crossref · retrieved 2026-09-27T02:46:43.528Z