Autor según el artículo: Miranda, J; Paules, C; Noell, G; Youssef, L; Paternina-Caicedo, A; Crovetto, F; Cañellas, N; Garcia-Martín, ML; Amigó, N; Eixarch, E; Faner, R; Figueras, F; Simos, R; Crispi, F; Gratacós, E
Departamento: Enginyeria Electrònica, Elèctrica i Automàtica
Autor/es de la URV: Cañellas Alberich, Nicolau
Palabras clave: Term Pregnancy Preeclampsia Nmr-spectroscopy Natriuretic peptide Management Fetal-growth restriction Early-onset Doppler Consequences
Resumen: Fetal growth restriction (FGR) affects 5-10% of pregnancies, is the largest contributor to fetal death, and can have long-term consequences for the child. Implementation of a standard clinical classification system is hampered by the multiphenotypic spectrum of small fetuses with substantial differences in perinatal risks. Machine learning and multiomics data can potentially revolutionize clinical decision-making in FGR by identifying new phenotypes. Herein, we describe a cluster analysis of FGR based on an unbiased machine-learning method. Our results confirm the existence of two subtypes of human FGR with distinct molecular and clinical features based on multiomic analysis. In addition, we demonstrated that clusters generated by machine learning significantly outperform single data subtype analysis and biologically support the current clinical classification in predicting adverse maternal and neonatal outcomes. Our approach can aid in the refinement of clinical classification systems for FGR supported by molecular and clinical signatures.
Áreas temáticas: Multidisciplinary sciences Multidisciplinary
Acceso a la licencia de uso: https://creativecommons.org/licenses/by/3.0/es/
Direcció de correo del autor: nicolau.canyellas@urv.cat
Identificador del autor: 0000-0003-4856-8132
Fecha de alta del registro: 2024-08-03
Versión del articulo depositado: info:eu-repo/semantics/publishedVersion
Enlace a la fuente original: https://www.sciencedirect.com/science/article/pii/S2589004223016978
URL Documento de licencia: https://repositori.urv.cat/ca/proteccio-de-dades/
Referencia al articulo segun fuente origial: Iscience. 26 (9):
Referencia de l'ítem segons les normes APA: Miranda, J; Paules, C; Noell, G; Youssef, L; Paternina-Caicedo, A; Crovetto, F; Cañellas, N; Garcia-Martín, ML; Amigó, N; Eixarch, E; Faner, R; Figuer (2023). Similarity network fusion to identify phenotypes of small-for-gestational-age fetuses. Iscience, 26(9), -. DOI: 10.1016/j.isci.2023.107620
DOI del artículo: 10.1016/j.isci.2023.107620
Entidad: Universitat Rovira i Virgili
Año de publicación de la revista: 2023
Tipo de publicación: Journal Publications