Driving innovation for rare skin cancers: utilizing common tumours and machine learning to predict immune checkpoint inhibitor response.

Hooiveld-Noeken JS, Fehrmann RSN, de Vries EGE, Jalving M

Open source

DOI
10.1016/j.iotech.2019.11.002
Published
2019 Dec
Container
Immuno-oncology technology
Publisher
Not recorded
Open access
yes

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BibTeX

@article{allodium:10.1016/j.iotech.2019.11.002,
  title = {Driving innovation for rare skin cancers: utilizing common tumours and machine learning to predict immune checkpoint inhibitor response.},
  author = {Hooiveld-Noeken JS and Fehrmann RSN and de Vries EGE and Jalving M},
  year = {2019},
  journal = {Immuno-oncology technology},
  doi = {10.1016/j.iotech.2019.11.002},
  url = {https://doi.org/10.1016/j.iotech.2019.11.002}
}

RIS

TY  - JOUR
TI  - Driving innovation for rare skin cancers: utilizing common tumours and machine learning to predict immune checkpoint inhibitor response.
AU  - Hooiveld-Noeken JS
AU  - Fehrmann RSN
AU  - de Vries EGE
AU  - Jalving M
PY  - 2019
JO  - Immuno-oncology technology
DO  - 10.1016/j.iotech.2019.11.002
UR  - https://doi.org/10.1016/j.iotech.2019.11.002
ER  -

APA

JS, H., RSN, F., EGE, D. V., & M, J. (2019). Driving innovation for rare skin cancers: utilizing common tumours and machine learning to predict immune checkpoint inhibitor response.. Immuno-oncology technology. https://doi.org/10.1016/j.iotech.2019.11.002

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