Machine learning characterization of cancer patients-derived extracellular vesicles using vibrational spectroscopies: results from a pilot study.
- DOI
- 10.1007/s10489-022-03203-1
- Published
- 2022-02-11
- Container
- Applied Intelligence
- Publisher
- Springer Science and Business Media LLC
- Open access
- unknown
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Cite this work
BibTeX
@article{allodium:10.1007/s10489-022-03203-1,
title = {Machine learning characterization of cancer patients-derived extracellular vesicles using vibrational spectroscopies: results from a pilot study.},
author = {Abicumaran Uthamacumaran and Samir Elouatik and Mohamed Abdouh and Melissa Berteau-Rainville and Zu-hua Gao and Goffredo Arena},
year = {2022},
journal = {Applied Intelligence},
doi = {10.1007/s10489-022-03203-1},
url = {https://doi.org/10.1007/s10489-022-03203-1}
}RIS
TY - JOUR TI - Machine learning characterization of cancer patients-derived extracellular vesicles using vibrational spectroscopies: results from a pilot study. AU - Abicumaran Uthamacumaran AU - Samir Elouatik AU - Mohamed Abdouh AU - Melissa Berteau-Rainville AU - Zu-hua Gao AU - Goffredo Arena PY - 2022 JO - Applied Intelligence DO - 10.1007/s10489-022-03203-1 UR - https://doi.org/10.1007/s10489-022-03203-1 ER -
APA
Uthamacumaran, A., Elouatik, S., Abdouh, M., Berteau-Rainville, M., Gao, Z., & Arena, G. (2022). Machine learning characterization of cancer patients-derived extracellular vesicles using vibrational spectroscopies: results from a pilot study.. Applied Intelligence. https://doi.org/10.1007/s10489-022-03203-1
Source records
- crossref · retrieved 2026-09-27T02:05:50.066Z