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Application of Machine Learning Solutions to Optimize Parameter Prediction to Enhance Automatic NMR Metabolite Profiling

  • Datos identificativos

    Identificador: imarina:9248790
    Autores:
    Canueto, DanielSalek, Reza MBullo, MonicaCorreig, XavierCanellas, Nicolau
    Resumen:
    The quality of automatic metabolite profiling in NMR datasets from complex matrices can be affected by the numerous sources of variability. These sources, as well as the presence of multiple low-intensity signals, cause uncertainty in the metabolite signal parameters. Lineshape fitting approaches often produce suboptimal resolutions to adapt them in a complex spectrum lineshape. As a result, the use of software tools for automatic profiling tends to be restricted to specific biological matrices and/or sample preparation protocols to obtain reliable results. However, the analysis and modelling of the signal parameters collected during initial iteration can be further optimized to reduce uncertainty by generating narrow and accurate predictions of the expected signal parameters. In this study, we show that, thanks to the predictions generated, better profiling quality indicators can be outputted, and the performance of automatic profiling can be maximized. Our proposed workflow can learn and model the sample properties; therefore, restrictions in the biological matrix, or sample preparation protocol, and limitations of lineshape fitting approaches can be overcome.
  • Otros:

    Autor según el artículo: Canueto, Daniel; Salek, Reza M; Bullo, Monica; Correig, Xavier; Canellas, Nicolau
    Departamento: Enginyeria Electrònica, Elèctrica i Automàtica
    Autor/es de la URV: Bulló Bonet, Mònica / Cañellas Alberich, Nicolau / Correig Blanchar, Francesc Xavier
    Palabras clave: Spectra Profiling Nmr Machine learning Automatic profiling quantification nmr mixtures metabolomics machine learning deconvolution 1d
    Resumen: The quality of automatic metabolite profiling in NMR datasets from complex matrices can be affected by the numerous sources of variability. These sources, as well as the presence of multiple low-intensity signals, cause uncertainty in the metabolite signal parameters. Lineshape fitting approaches often produce suboptimal resolutions to adapt them in a complex spectrum lineshape. As a result, the use of software tools for automatic profiling tends to be restricted to specific biological matrices and/or sample preparation protocols to obtain reliable results. However, the analysis and modelling of the signal parameters collected during initial iteration can be further optimized to reduce uncertainty by generating narrow and accurate predictions of the expected signal parameters. In this study, we show that, thanks to the predictions generated, better profiling quality indicators can be outputted, and the performance of automatic profiling can be maximized. Our proposed workflow can learn and model the sample properties; therefore, restrictions in the biological matrix, or sample preparation protocol, and limitations of lineshape fitting approaches can be overcome.
    Áreas temáticas: Molecular biology Medicina ii Farmacia Endocrinology, diabetes and metabolism Ciências biológicas ii Ciências biológicas i Biotecnología Biochemistry & molecular biology Biochemistry
    Acceso a la licencia de uso: https://creativecommons.org/licenses/by/3.0/es/
    Direcció de correo del autor: monica.bullo@urv.cat xavier.correig@urv.cat nicolau.canyellas@urv.cat
    Identificador del autor: 0000-0002-0218-7046 0000-0002-6902-3054 0000-0003-4856-8132
    Fecha de alta del registro: 2024-10-12
    Versión del articulo depositado: info:eu-repo/semantics/publishedVersion
    Enlace a la fuente original: https://www.mdpi.com/2218-1989/12/4/283
    URL Documento de licencia: https://repositori.urv.cat/ca/proteccio-de-dades/
    Referencia al articulo segun fuente origial: Metabolites. 12 (4): 283-
    Referencia de l'ítem segons les normes APA: Canueto, Daniel; Salek, Reza M; Bullo, Monica; Correig, Xavier; Canellas, Nicolau (2022). Application of Machine Learning Solutions to Optimize Parameter Prediction to Enhance Automatic NMR Metabolite Profiling. Metabolites, 12(4), 283-. DOI: 10.3390/metabo12040283
    DOI del artículo: 10.3390/metabo12040283
    Entidad: Universitat Rovira i Virgili
    Año de publicación de la revista: 2022
    Tipo de publicación: Journal Publications
  • Palabras clave:

    Biochemistry,Biochemistry & Molecular Biology,Endocrinology, Diabetes and Metabolism,Molecular Biology
    Spectra
    Profiling
    Nmr
    Machine learning
    Automatic profiling
    quantification
    nmr
    mixtures
    metabolomics
    machine learning
    deconvolution
    1d
    Molecular biology
    Medicina ii
    Farmacia
    Endocrinology, diabetes and metabolism
    Ciências biológicas ii
    Ciências biológicas i
    Biotecnología
    Biochemistry & molecular biology
    Biochemistry
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