Author, as appears in the article.: Canueto, Daniel; Salek, Reza M; Bullo, Monica; Correig, Xavier; Canellas, Nicolau
Department: Enginyeria Electrònica, Elèctrica i Automàtica
URV's Author/s: Bulló Bonet, Mònica / Cañellas Alberich, Nicolau / Correig Blanchar, Francesc Xavier
Keywords: Spectra Profiling Nmr Machine learning Automatic profiling quantification nmr mixtures metabolomics machine learning deconvolution 1d
Abstract: 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.
Thematic Areas: Molecular biology Medicina ii Farmacia Endocrinology, diabetes and metabolism Ciências biológicas ii Ciências biológicas i Biotecnología Biochemistry & molecular biology Biochemistry
licence for use: https://creativecommons.org/licenses/by/3.0/es/
Author's mail: monica.bullo@urv.cat xavier.correig@urv.cat nicolau.canyellas@urv.cat
Author identifier: 0000-0002-0218-7046 0000-0002-6902-3054 0000-0003-4856-8132
Record's date: 2024-10-12
Papper version: info:eu-repo/semantics/publishedVersion
Link to the original source: https://www.mdpi.com/2218-1989/12/4/283
Licence document URL: https://repositori.urv.cat/ca/proteccio-de-dades/
Papper original source: Metabolites. 12 (4): 283-
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
Article's DOI: 10.3390/metabo12040283
Entity: Universitat Rovira i Virgili
Journal publication year: 2022
Publication Type: Journal Publications