AI redefines mass spectrometry chemicals identification: retention time prediction in metabolomics and for a Human Exposome Project.

Sillé FCM, Prasse C, Luechtefeld T, Hartung T

DOI: 10.3389/fpubh.2025.1687056
Published: 2025
Container: Frontiers in public health
Publisher: Not recorded
Open access: yes

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Cite this work

BibTeX

@article{allodium:10.3389/fpubh.2025.1687056,
  title = {AI redefines mass spectrometry chemicals identification: retention time prediction in metabolomics and for a Human Exposome Project.},
  author = {Sillé FCM and Prasse C and Luechtefeld T and Hartung T},
  year = {2025},
  journal = {Frontiers in public health},
  doi = {10.3389/fpubh.2025.1687056},
  url = {https://doi.org/10.3389/fpubh.2025.1687056}
}

RIS

TY  - JOUR
TI  - AI redefines mass spectrometry chemicals identification: retention time prediction in metabolomics and for a Human Exposome Project.
AU  - Sillé FCM
AU  - Prasse C
AU  - Luechtefeld T
AU  - Hartung T
PY  - 2025
JO  - Frontiers in public health
DO  - 10.3389/fpubh.2025.1687056
UR  - https://doi.org/10.3389/fpubh.2025.1687056
ER  -

APA

FCM, S., C, P., T, L., & T, H. (2025). AI redefines mass spectrometry chemicals identification: retention time prediction in metabolomics and for a Human Exposome Project.. Frontiers in public health. https://doi.org/10.3389/fpubh.2025.1687056

Source records

pubmed · retrieved 2026-09-27T10:03:23.565Z