Interpretable machine learning for low-sample multi-omics: a case study of ferret vaccine response
- DOI
- 10.1093/bioadv/vbag167
- Published
- 2026
- Container
- Bioinformatics Advances
- Publisher
- Oxford University Press (OUP)
- Open access
- unknown
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Cite this work
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
Source records
- crossref · retrieved 2026-09-25T04:56:29.378Z