Tipo de documento: info:eu-repo/semantics/other
DOI: 10.5281/zenodo.8176373
Publicaciones relacionadas: Miranda, J., Simões, R. V., Paules, C., Cañueto, D., Pardo-Cea, M. A., García-Martín, M. L., Crovetto, F., Fuertes-Martin, R., Domenech, M., Gómez-Roig, M. D., Eixarch, E., Estruch, R., Hansson, S. R., Amigó, N., Cañellas, N., Crispi, F., & Gratacós, E. (2018). Metabolic profiling and targeted lipidomics reveals a disturbed lipid profile in mothers and fetuses with intrauterine growth restriction. Scientific Reports, 8(1), 13614. https://doi.org/10.1038/s41598-018-31832-5
Grupo de investigación: NanoElectronic and PHOtonic Systems
Departamento: Enginyeria Electrònica, Elèctrica i Automàtica
Autor: Cañellas, Nicolau
Fecha alta repositorio: 2023-07-24
Año de publicación de la dataset: 2023
Materia: Medicina
Identificador del investigador: 0000-0003-4856-8132
DOI de la publicación relacionada: Similarity network fusion to identify phenotypes of small for gestational age fetuses
Idioma: en
Publicado por (editorial): Universitat Rovira i Virgili (URV)
Derechos de acceso: info:eu-repo/semantics/openAccess
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 multi-omic 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.
Repositori URV