Multi-omic analysis of deep learning-derived phenotypes links ophthalmic imaging to cardiovascular and neurological traits.

Julian TH, Dou H, Duan J, Huang J, Yoo E, Green DJ, Strange A, Alhathli E, Sperrin M, Keane PA, Chew EY, Keavney B, Fitzgerald TW, Cooper-Knock J, Birney E, Frangi AF, Sergouniotis PI, UK Biobank Eye and Vision Consortium

Open source

DOI
10.1038/s44161-026-00815-5
Published
2026 Jun
Container
Nature cardiovascular research
Publisher
Not recorded
Open access
yes

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BibTeX

@article{allodium:10.1038/s44161-026-00815-5,
  title = {Multi-omic analysis of deep learning-derived phenotypes links ophthalmic imaging to cardiovascular and neurological traits.},
  author = {Julian TH and Dou H and Duan J and Huang J and Yoo E and Green DJ and Strange A and Alhathli E and Sperrin M and Keane PA and Chew EY and Keavney B and Fitzgerald TW and Cooper-Knock J and Birney E and Frangi AF and Sergouniotis PI and UK Biobank Eye and Vision Consortium},
  year = {2026},
  journal = {Nature cardiovascular research},
  doi = {10.1038/s44161-026-00815-5},
  url = {https://doi.org/10.1038/s44161-026-00815-5}
}

RIS

TY  - JOUR
TI  - Multi-omic analysis of deep learning-derived phenotypes links ophthalmic imaging to cardiovascular and neurological traits.
AU  - Julian TH
AU  - Dou H
AU  - Duan J
AU  - Huang J
AU  - Yoo E
AU  - Green DJ
AU  - Strange A
AU  - Alhathli E
AU  - Sperrin M
AU  - Keane PA
AU  - Chew EY
AU  - Keavney B
AU  - Fitzgerald TW
AU  - Cooper-Knock J
AU  - Birney E
AU  - Frangi AF
AU  - Sergouniotis PI
AU  - UK Biobank Eye and Vision Consortium
PY  - 2026
JO  - Nature cardiovascular research
DO  - 10.1038/s44161-026-00815-5
UR  - https://doi.org/10.1038/s44161-026-00815-5
ER  - 

APA

TH, J., H, D., J, D., J, H., E, Y., DJ, G., A, S., E, A., M, S., PA, K., EY, C., B, K., TW, F., J, C., E, B., AF, F., PI, S., & Consortium, U. B. E. A. V. (2026). Multi-omic analysis of deep learning-derived phenotypes links ophthalmic imaging to cardiovascular and neurological traits.. Nature cardiovascular research. https://doi.org/10.1038/s44161-026-00815-5

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