Author, as appears in the 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
Department: Enginyeria Electrònica, Elèctrica i Automàtica
URV's Author/s: Cañellas Alberich, Nicolau
Keywords: Term Pregnancy Preeclampsia Nmr-spectroscopy Natriuretic peptide Management Fetal-growth restriction Early-onset Doppler Consequences
Abstract: 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.
Thematic Areas: Multidisciplinary sciences Multidisciplinary
licence for use: https://creativecommons.org/licenses/by/3.0/es/
Author's mail: nicolau.canyellas@urv.cat
Author identifier: 0000-0003-4856-8132
Record's date: 2024-08-03
Papper version: info:eu-repo/semantics/publishedVersion
Link to the original source: https://www.sciencedirect.com/science/article/pii/S2589004223016978
Licence document URL: https://repositori.urv.cat/ca/proteccio-de-dades/
Papper original source: Iscience. 26 (9):
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
Article's DOI: 10.1016/j.isci.2023.107620
Entity: Universitat Rovira i Virgili
Journal publication year: 2023
Publication Type: Journal Publications
Repositori URV