Peer-reviewedCC BY-NC-ND 4.0BMC Medicine
Discovering latent phenotypic signatures of preterm birth and genetic risk using tensor decomposition on electronic health records
Related
- Discovering gut microbiome signatures that predict adverse and reverse cardiac remodeling in preclinical heart failure
- Exploratory Integration of Metabolomics and Machine Learning Identifies Candidate Signatures of Spontaneous Preterm Birth
- Electronic health data exploring cardiorespiratory responses of transfusions in preterm infants: An international multicenter cohort study
- Phenotypic characteristics associated with preterm births in the Indian population.
- Immune and body composition signatures of neurodevelopmental impairments in preterm preschoolers
BibTeX
@article{abraham2026discovering,
title = {{Discovering latent phenotypic signatures of preterm birth and genetic risk using tensor decomposition on electronic health records}},
author = {Abin Abraham and Hannah Takasuka and Cosmin Bejan and David Rinker and Antonis Rokas and Marina Sirota and John A. Capra},
journal = {BMC Medicine},
year = {2026},
month = oct,
doi = {10.1186/s12916-026-05203-1},
url = {https://link.springer.com/10.1186/s12916-026-05203-1},
note = {CC BY-NC-ND 4.0},
}
RIS
TY - JOUR
TI - Discovering latent phenotypic signatures of preterm birth and genetic risk using tensor decomposition on electronic health records
AU - Abin Abraham
AU - Hannah Takasuka
AU - Cosmin Bejan
AU - David Rinker
AU - Antonis Rokas
AU - Marina Sirota
AU - John A. Capra
PY - 2026
DA - 2026/10/01
JO - BMC Medicine
DO - 10.1186/s12916-026-05203-1
UR - https://link.springer.com/10.1186/s12916-026-05203-1
SN - 1741-7015
ER -