Consistency of feature attribution in deep learning architectures for multi-omics
- DOI
- 10.1038/s41598-026-58312-5
- Published
- 2026-08-18
- Container
- Scientific Reports
- Publisher
- Springer Science and Business Media LLC
- Open access
- unknown
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Cite this work
BibTeX
@article{allodium:10.1038/s41598-026-58312-5,
title = {Consistency of feature attribution in deep learning architectures for multi-omics},
author = {Daniel Claborne and Javier Flores and Samantha Erwin and Luke Durell and David Degnan and Rachel Richardson and Ruby Fore and Lisa Bramer},
year = {2026},
journal = {Scientific Reports},
doi = {10.1038/s41598-026-58312-5},
url = {https://doi.org/10.1038/s41598-026-58312-5}
}RIS
TY - JOUR TI - Consistency of feature attribution in deep learning architectures for multi-omics AU - Daniel Claborne AU - Javier Flores AU - Samantha Erwin AU - Luke Durell AU - David Degnan AU - Rachel Richardson AU - Ruby Fore AU - Lisa Bramer PY - 2026 JO - Scientific Reports DO - 10.1038/s41598-026-58312-5 UR - https://doi.org/10.1038/s41598-026-58312-5 ER -
APA
Claborne, D., Flores, J., Erwin, S., Durell, L., Degnan, D., Richardson, R., Fore, R., & Bramer, L. (2026). Consistency of feature attribution in deep learning architectures for multi-omics. Scientific Reports. https://doi.org/10.1038/s41598-026-58312-5
Source records
- crossref · retrieved 2026-09-26T06:30:59.294Z