Interpretable deep survival analysis of Alzheimer's disease via metabolic genetic variants.

Goo S, Lee S, Chae JW, Jung S, Yun HY

DOI: 10.1093/bioinformatics/btag213
Published: 2026 Jun 1
Container: Bioinformatics (Oxford, England)
Publisher: Not recorded
Open access: yes

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Cite this work

BibTeX

@article{allodium:10.1093/bioinformatics/btag213,
  title = {Interpretable deep survival analysis of Alzheimer's disease via metabolic genetic variants.},
  author = {Goo S and Lee S and Chae JW and Jung S and Yun HY},
  year = {2026},
  journal = {Bioinformatics (Oxford, England)},
  doi = {10.1093/bioinformatics/btag213},
  url = {https://doi.org/10.1093/bioinformatics/btag213}
}

RIS

TY  - JOUR
TI  - Interpretable deep survival analysis of Alzheimer's disease via metabolic genetic variants.
AU  - Goo S
AU  - Lee S
AU  - Chae JW
AU  - Jung S
AU  - Yun HY
PY  - 2026
JO  - Bioinformatics (Oxford, England)
DO  - 10.1093/bioinformatics/btag213
UR  - https://doi.org/10.1093/bioinformatics/btag213
ER  -

APA

S, G., S, L., JW, C., S, J., & HY, Y. (2026). Interpretable deep survival analysis of Alzheimer's disease via metabolic genetic variants.. Bioinformatics (Oxford, England). https://doi.org/10.1093/bioinformatics/btag213

Source records

pubmed · retrieved 2026-09-26T22:31:24.696Z