Non-invasive prediction of embryo ploidy status using metabolomic profiling and machine learning

Ryan Walsh, Luis Mancera, Ali Ahmady, Nabil Arrach

Open source

DOI: 10.1016/j.rbmo.2026.105892
Published: 2026-11
Container: Reproductive BioMedicine Online
Publisher: Elsevier BV
Open access: unknown

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Cite this work

BibTeX

@article{allodium:10.1016/j.rbmo.2026.105892,
  title = {Non-invasive prediction of embryo ploidy status using metabolomic profiling and machine learning},
  author = {Ryan Walsh and Luis Mancera and Ali Ahmady and Nabil Arrach},
  year = {2026},
  journal = {Reproductive BioMedicine Online},
  doi = {10.1016/j.rbmo.2026.105892},
  url = {https://doi.org/10.1016/j.rbmo.2026.105892}
}

RIS

TY  - JOUR
TI  - Non-invasive prediction of embryo ploidy status using metabolomic profiling and machine learning
AU  - Ryan Walsh
AU  - Luis Mancera
AU  - Ali Ahmady
AU  - Nabil Arrach
PY  - 2026
JO  - Reproductive BioMedicine Online
DO  - 10.1016/j.rbmo.2026.105892
UR  - https://doi.org/10.1016/j.rbmo.2026.105892
ER  -

APA

Walsh, R., Mancera, L., Ahmady, A., & Arrach, N. (2026). Non-invasive prediction of embryo ploidy status using metabolomic profiling and machine learning. Reproductive BioMedicine Online. https://doi.org/10.1016/j.rbmo.2026.105892

Source records

crossref · retrieved 2026-09-25T06:27:27.953Z