On the feasibility of deep learning applications using raw mass spectrometry data
- DOI
- 10.1093/bioinformatics/btab311
- Published
- 2021-07-01
- Container
- Bioinformatics
- Publisher
- Oxford University Press (OUP)
- Open access
- unknown
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Cite this work
BibTeX
@article{allodium:10.1093/bioinformatics/btab311,
title = {On the feasibility of deep learning applications using raw mass spectrometry data},
author = {Joris Cadow and Matteo Manica and Roland Mathis and Roger R Reddel and Phillip J Robinson and Peter J Wild and Peter G Hains and Natasha Lucas and Qing Zhong and Tiannan Guo and Ruedi Aebersold and María Rodríguez Martínez},
year = {2021},
journal = {Bioinformatics},
doi = {10.1093/bioinformatics/btab311},
url = {https://doi.org/10.1093/bioinformatics/btab311}
}RIS
TY - JOUR TI - On the feasibility of deep learning applications using raw mass spectrometry data AU - Joris Cadow AU - Matteo Manica AU - Roland Mathis AU - Roger R Reddel AU - Phillip J Robinson AU - Peter J Wild AU - Peter G Hains AU - Natasha Lucas AU - Qing Zhong AU - Tiannan Guo AU - Ruedi Aebersold AU - María Rodríguez Martínez PY - 2021 JO - Bioinformatics DO - 10.1093/bioinformatics/btab311 UR - https://doi.org/10.1093/bioinformatics/btab311 ER -
APA
Cadow, J., Manica, M., Mathis, R., Reddel, R. R., Robinson, P. J., Wild, P. J., Hains, P. G., Lucas, N., Zhong, Q., Guo, T., Aebersold, R., & Martínez, M. R. (2021). On the feasibility of deep learning applications using raw mass spectrometry data. Bioinformatics. https://doi.org/10.1093/bioinformatics/btab311
Source records
- crossref · retrieved 2026-09-25T03:16:58.048Z