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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...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2022
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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. |
format | Online Article Text |
id | pubmed-9027668 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
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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