Harnessing Fc/FcRn Affinity Data from Patents with Different Machine Learning Methods.

Dumet C, Pugnière M, Henriquet C, Gouilleux-Gruart V, Poupon A, Watier H

DOI: 10.3390/ijms24065724
Published: 2023 Mar 16
Container: International journal of molecular sciences
Publisher: Not recorded
Open access: yes

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Cite this work

BibTeX

@article{allodium:10.3390/ijms24065724,
  title = {Harnessing Fc/FcRn Affinity Data from Patents with Different Machine Learning Methods.},
  author = {Dumet C and Pugnière M and Henriquet C and Gouilleux-Gruart V and Poupon A and Watier H},
  year = {2023},
  journal = {International journal of molecular sciences},
  doi = {10.3390/ijms24065724},
  url = {https://doi.org/10.3390/ijms24065724}
}

RIS

TY  - JOUR
TI  - Harnessing Fc/FcRn Affinity Data from Patents with Different Machine Learning Methods.
AU  - Dumet C
AU  - Pugnière M
AU  - Henriquet C
AU  - Gouilleux-Gruart V
AU  - Poupon A
AU  - Watier H
PY  - 2023
JO  - International journal of molecular sciences
DO  - 10.3390/ijms24065724
UR  - https://doi.org/10.3390/ijms24065724
ER  -

APA

C, D., M, P., C, H., V, G., A, P., & H, W. (2023). Harnessing Fc/FcRn Affinity Data from Patents with Different Machine Learning Methods.. International journal of molecular sciences. https://doi.org/10.3390/ijms24065724

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pubmed · retrieved 2026-09-26T15:46:41.398Z