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

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 of...

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Detalles Bibliográficos
Autores principales: Cañueto, Daniel, Salek, Reza M., Bulló, Mònica, Correig, Xavier, Cañellas, Nicolau
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9027668/
https://www.ncbi.nlm.nih.gov/pubmed/35448470
http://dx.doi.org/10.3390/metabo12040283
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author Cañueto, Daniel
Salek, Reza M.
Bulló, Mònica
Correig, Xavier
Cañellas, Nicolau
author_facet Cañueto, Daniel
Salek, Reza M.
Bulló, Mònica
Correig, Xavier
Cañellas, Nicolau
author_sort Cañueto, Daniel
collection PubMed
description 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.
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spelling pubmed-90276682022-04-23 Application of Machine Learning Solutions to Optimize Parameter Prediction to Enhance Automatic NMR Metabolite Profiling Cañueto, Daniel Salek, Reza M. Bulló, Mònica Correig, Xavier Cañellas, Nicolau Metabolites Article 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. MDPI 2022-03-24 /pmc/articles/PMC9027668/ /pubmed/35448470 http://dx.doi.org/10.3390/metabo12040283 Text en © 2022 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Cañueto, Daniel
Salek, Reza M.
Bulló, Mònica
Correig, Xavier
Cañellas, Nicolau
Application of Machine Learning Solutions to Optimize Parameter Prediction to Enhance Automatic NMR Metabolite Profiling
title Application of Machine Learning Solutions to Optimize Parameter Prediction to Enhance Automatic NMR Metabolite Profiling
title_full Application of Machine Learning Solutions to Optimize Parameter Prediction to Enhance Automatic NMR Metabolite Profiling
title_fullStr Application of Machine Learning Solutions to Optimize Parameter Prediction to Enhance Automatic NMR Metabolite Profiling
title_full_unstemmed Application of Machine Learning Solutions to Optimize Parameter Prediction to Enhance Automatic NMR Metabolite Profiling
title_short Application of Machine Learning Solutions to Optimize Parameter Prediction to Enhance Automatic NMR Metabolite Profiling
title_sort application of machine learning solutions to optimize parameter prediction to enhance automatic nmr metabolite profiling
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9027668/
https://www.ncbi.nlm.nih.gov/pubmed/35448470
http://dx.doi.org/10.3390/metabo12040283
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