Similarity network fusion to identify phenotypes of small for gestational age fetuses

Cañellas, Nicolau

Pertenece a la colección PC:1236

TÍTULO:

Similarity network fusion to identify phenotypes of small for gestational age fetuses - PC:4076

Handle:

https://hdl.handle.net/20.500.11797/PC4076

Autor:	Cañellas, Nicolau
Publicado por (editorial):	Universitat Rovira i Virgili (URV)
Tipo de documento:	info:eu-repo/semantics/other
Departamento:	Enginyeria Electrònica, Elèctrica i Automàtica
DOI de la publicación relacionada:	Similarity network fusion to identify phenotypes of small for gestational age fetuses
Materia:	Medicina
Derechos de acceso:	info:eu-repo/semantics/openAccess
Año de publicación de la dataset:	2023
DOI:	10.5281/zenodo.8176373
Identificador del investigador:	0000-0003-4856-8132
Grupo de investigación:	NanoElectronic and PHOtonic Systems
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
Palabras clave:	Fetal Growth Restriction, Disturbed Lipid Profile, Very Low Density Lipoproteins (VLDL), Cord Blood Plasma, Small Fetuses
Título del conjunto de datos:	Similarity network fusion to identify phenotypes of small for gestational age fetuses

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Descripción:	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.
Tipo:	info:eu-repo/semantics/other
Coautor:	Enginyeria Electrònica, Elèctrica i Automàtica NanoElectronic and PHOtonic Systems
Títol:	Similarity network fusion to identify phenotypes of small for gestational age fetuses
Materia:	Medicina Fetal Growth Restriction, Disturbed Lipid Profile, Very Low Density Lipoproteins (VLDL), Cord Blood Plasma, Small Fetuses
Formato:	Universitat Rovira i Virgili (URV)
Autor:	Cañellas, Nicolau
Derechos:	info:eu-repo/semantics/openAccess
Fecha:	2023
Editor:	Universitat Rovira i Virgili (URV)
Fecha alta repositorio:	2023-07-24

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