Autor segons l'article: 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
Departament: Enginyeria Electrònica, Elèctrica i Automàtica
Autor/s de la URV: Cañellas Alberich, Nicolau
Paraules clau: Term Pregnancy Preeclampsia Nmr-spectroscopy Natriuretic peptide Management Fetal-growth restriction Early-onset Doppler Consequences
Resum: 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.
Àrees temàtiques: Multidisciplinary sciences Multidisciplinary
Accès a la llicència d'ús: https://creativecommons.org/licenses/by/3.0/es/
Adreça de correu electrònic de l'autor: nicolau.canyellas@urv.cat
Identificador de l'autor: 0000-0003-4856-8132
Data d'alta del registre: 2024-08-03
Versió de l'article dipositat: info:eu-repo/semantics/publishedVersion
Enllaç font original: https://www.sciencedirect.com/science/article/pii/S2589004223016978
URL Document de llicència: https://repositori.urv.cat/ca/proteccio-de-dades/
Referència a l'article segons font original: Iscience. 26 (9):
Referència 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 de l'article: 10.1016/j.isci.2023.107620
Entitat: Universitat Rovira i Virgili
Any de publicació de la revista: 2023
Tipus de publicació: Journal Publications