MCOD: a memory-constrained deep learning framework for robust outlier detection in quantitative proteomics
- DOI
- 10.1093/bib/bbag507
- Published
- 2026-09-01
- Container
- Briefings in Bioinformatics
- Publisher
- Oxford University Press (OUP)
- Open access
- unknown
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Cite this work
BibTeX
@article{allodium:10.1093/bib/bbag507,
title = {MCOD: a memory-constrained deep learning framework for robust outlier detection in quantitative proteomics},
author = {Jinze Huang and Huanyue Liao and Bo Meng and Guangkui Fan and Dong An and Xinhua Dai and Xiang Fang and Yang Zhao},
year = {2026},
journal = {Briefings in Bioinformatics},
doi = {10.1093/bib/bbag507},
url = {https://doi.org/10.1093/bib/bbag507}
}RIS
TY - JOUR TI - MCOD: a memory-constrained deep learning framework for robust outlier detection in quantitative proteomics AU - Jinze Huang AU - Huanyue Liao AU - Bo Meng AU - Guangkui Fan AU - Dong An AU - Xinhua Dai AU - Xiang Fang AU - Yang Zhao PY - 2026 JO - Briefings in Bioinformatics DO - 10.1093/bib/bbag507 UR - https://doi.org/10.1093/bib/bbag507 ER -
APA
Huang, J., Liao, H., Meng, B., Fan, G., An, D., Dai, X., Fang, X., & Zhao, Y. (2026). MCOD: a memory-constrained deep learning framework for robust outlier detection in quantitative proteomics. Briefings in Bioinformatics. https://doi.org/10.1093/bib/bbag507
Source records
- crossref · retrieved 2026-09-27T13:12:58.092Z