Conjunts de dades de producció científica> Enginyeria Electrònica, Elèctrica i Automàtica

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

  • Dades identificatives

    Identificador: PC:4076
    Handle: https://hdl.handle.net/20.500.11797/PC4076
    Autors:
    Cañellas, Nicolau
    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 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.

  • Altres:

    Matèria: Medicina
    Drets d'accés: info:eu-repo/semantics/openAccess
    Identificador del investigador: 0000-0003-4856-8132
    Publicat per (editora): Universitat Rovira i Virgili (URV)
    Publicacions relacionades: 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
    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 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.
    Departament: Enginyeria Electrònica, Elèctrica i Automàtica
    DOI: 10.5281/zenodo.8176373
    Tipus de document: info:eu-repo/semantics/other
    DOI de la publicació relacionada: Similarity network fusion to identify phenotypes of small for gestational age fetuses
    Data alta repositori: 2023-07-24
    Autor: Cañellas, Nicolau
    Paraules clau: Fetal Growth Restriction, Disturbed Lipid Profile, Very Low Density Lipoproteins (VLDL), Cord Blood Plasma, Small Fetuses
    Grup de recerca: NanoElectronic and PHOtonic Systems
    Any de publicació de la dataset: 2023
    Títol del conjunt de dades: Similarity network fusion to identify phenotypes of small for gestational age fetuses

  • Paraules clau:

    Medicina
    Fetal Growth Restriction, Disturbed Lipid Profile, Very Low Density Lipoproteins (VLDL), Cord Blood Plasma, Small Fetuses

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