Machine Learning-Enabled Metallomics Reveals Geographic Exposomic Signatures in a Large Brazilian Cohort.

Morais DA, de Sousa Júnior WT, de Salles GP, Souza MCO, Domingo JL, Lotufo P, Benseñor IM, Barbosa F.

DOI: 10.1021/envhealth.6c00155
Published: 2026-05-20
Container: Environ Health (Wash)
Publisher: Not recorded
Open access: yes

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Cite this work

BibTeX

@article{allodium:10.1021/envhealth.6c00155,
  title = {Machine Learning-Enabled Metallomics Reveals Geographic Exposomic Signatures in a Large Brazilian Cohort.},
  author = {Morais DA and  de Sousa Júnior WT and  de Salles GP and  Souza MCO and  Domingo JL and  Lotufo P and  Benseñor IM and  Barbosa F.},
  year = {2026},
  journal = {Environ Health (Wash)},
  doi = {10.1021/envhealth.6c00155},
  url = {https://doi.org/10.1021/envhealth.6c00155}
}

RIS

TY  - JOUR
TI  - Machine Learning-Enabled Metallomics Reveals Geographic Exposomic Signatures in a Large Brazilian Cohort.
AU  - Morais DA
AU  -  de Sousa Júnior WT
AU  -  de Salles GP
AU  -  Souza MCO
AU  -  Domingo JL
AU  -  Lotufo P
AU  -  Benseñor IM
AU  -  Barbosa F.
PY  - 2026
JO  - Environ Health (Wash)
DO  - 10.1021/envhealth.6c00155
UR  - https://doi.org/10.1021/envhealth.6c00155
ER  -

APA

DA, M., WT, D. S. J., GP, D. S., MCO, S., JL, D., P, L., IM, B., & F., B. (2026). Machine Learning-Enabled Metallomics Reveals Geographic Exposomic Signatures in a Large Brazilian Cohort.. Environ Health (Wash). https://doi.org/10.1021/envhealth.6c00155

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europe-pmc · retrieved 2026-09-26T13:54:56.299Z