Interpretable machine learning for low-sample multi-omics: a case study of ferret vaccine response

Nehleh Kargarfard, Robert Dunne, Carol Lee, Laurence Wilson, Alexander J McAuley

Open source

DOI
10.1093/bioadv/vbag167
Published
2026
Container
Bioinformatics Advances
Publisher
Oxford University Press (OUP)
Open access
unknown

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BibTeX

@article{allodium:10.1093/bioadv/vbag167,
  title = {Interpretable machine learning for low-sample multi-omics: a case study of ferret vaccine response},
  author = {Nehleh Kargarfard and Robert Dunne and Carol Lee and Laurence Wilson and Alexander J McAuley},
  year = {2026},
  journal = {Bioinformatics Advances},
  doi = {10.1093/bioadv/vbag167},
  url = {https://doi.org/10.1093/bioadv/vbag167}
}

RIS

TY  - JOUR
TI  - Interpretable machine learning for low-sample multi-omics: a case study of ferret vaccine response
AU  - Nehleh Kargarfard
AU  - Robert Dunne
AU  - Carol Lee
AU  - Laurence Wilson
AU  - Alexander J McAuley
PY  - 2026
JO  - Bioinformatics Advances
DO  - 10.1093/bioadv/vbag167
UR  - https://doi.org/10.1093/bioadv/vbag167
ER  - 

APA

Kargarfard, N., Dunne, R., Lee, C., Wilson, L., & McAuley, A. J. (2026). Interpretable machine learning for low-sample multi-omics: a case study of ferret vaccine response. Bioinformatics Advances. https://doi.org/10.1093/bioadv/vbag167

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