Prediction of homologous recombination deficiency from routine histology with attention-based multiple instance learning in nine different tumor types.

Loeffler CML, El Nahhas OSM, Muti HS, Carrero ZI, Seibel T, van Treeck M, Cifci D, Gustav M, Bretz K, Gaisa NT, Lehmann KV, Leary A, Selenica P, Reis-Filho JS, Ortiz-Bruechle N, Kather JN

Open source

DOI
10.1186/s12915-024-02022-9
Published
2024 Oct 8
Container
BMC biology
Publisher
Not recorded
Open access
yes

Credibility signals

limited evidence Score 45/100 under policy 1.0.0. This is a metadata assessment, not a judgment of the paper's conclusions.

Show all credibility signals

Cite this work

BibTeX

@article{allodium:10.1186/s12915-024-02022-9,
  title = {Prediction of homologous recombination deficiency from routine histology with attention-based multiple instance learning in nine different tumor types.},
  author = {Loeffler CML and El Nahhas OSM and Muti HS and Carrero ZI and Seibel T and van Treeck M and Cifci D and Gustav M and Bretz K and Gaisa NT and Lehmann KV and Leary A and Selenica P and Reis-Filho JS and Ortiz-Bruechle N and Kather JN},
  year = {2024},
  journal = {BMC biology},
  doi = {10.1186/s12915-024-02022-9},
  url = {https://doi.org/10.1186/s12915-024-02022-9}
}

RIS

TY  - JOUR
TI  - Prediction of homologous recombination deficiency from routine histology with attention-based multiple instance learning in nine different tumor types.
AU  - Loeffler CML
AU  - El Nahhas OSM
AU  - Muti HS
AU  - Carrero ZI
AU  - Seibel T
AU  - van Treeck M
AU  - Cifci D
AU  - Gustav M
AU  - Bretz K
AU  - Gaisa NT
AU  - Lehmann KV
AU  - Leary A
AU  - Selenica P
AU  - Reis-Filho JS
AU  - Ortiz-Bruechle N
AU  - Kather JN
PY  - 2024
JO  - BMC biology
DO  - 10.1186/s12915-024-02022-9
UR  - https://doi.org/10.1186/s12915-024-02022-9
ER  - 

APA

CML, L., OSM, E. N., HS, M., ZI, C., T, S., M, V. T., D, C., M, G., K, B., NT, G., KV, L., A, L., P, S., JS, R., N, O., & JN, K. (2024). Prediction of homologous recombination deficiency from routine histology with attention-based multiple instance learning in nine different tumor types.. BMC biology. https://doi.org/10.1186/s12915-024-02022-9

Source records