دورية أكاديمية
أعرض في EDS

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

التفاصيل البيبلوغرافية
العنوان: Application of Machine Learning Solutions to Optimize Parameter Prediction to Enhance Automatic NMR Metabolite Profiling.
المؤلفون: Cañueto D; Department of Electronic Engineering and Automation, University Rovira i Virgili, 43007 Tarragona, Spain., Salek RM; Bruker BioSpin GmbH, Rudolf-Plank-Str. 23, 76275 Ettlingen, Germany., Bulló M; Department of Biochemistry and Biotechnology, Faculty of Medicine and Health Sciences, University Rovira i Virgili (URV), 43201 Reus, Spain.; Institut d'Investigació Sanitaria Pere Virgili (IISPV), Hospital Universitari Sant Joan de Reus, 43204 Reus, Spain.; Consorcio CIBER, M.P. Fisiopatología de la Obesidad y Nutrición (CIBERObn), Instituto de Salud Carlos III (ISCIII), 28029 Madrid, Spain., Correig X; Department of Electronic Engineering and Automation, University Rovira i Virgili, 43007 Tarragona, Spain.; Institut d'Investigació Sanitaria Pere Virgili (IISPV), Hospital Universitari Sant Joan de Reus, 43204 Reus, Spain.; Spanish Biomedical Research Centre in Diabetes and Associated Metabolic Disorders (CIBERDEM), 28029 Madrid, Spain., Cañellas N; Department of Electronic Engineering and Automation, University Rovira i Virgili, 43007 Tarragona, Spain.; Institut d'Investigació Sanitaria Pere Virgili (IISPV), Hospital Universitari Sant Joan de Reus, 43204 Reus, Spain.; Spanish Biomedical Research Centre in Diabetes and Associated Metabolic Disorders (CIBERDEM), 28029 Madrid, Spain.
المصدر: Metabolites [Metabolites] 2022 Mar 24; Vol. 12 (4). Date of Electronic Publication: 2022 Mar 24.
نوع المنشور: Journal Article
اللغة: English
بيانات الدورية: Publisher: MDPI Country of Publication: Switzerland NLM ID: 101578790 Publication Model: Electronic Cited Medium: Print ISSN: 2218-1989 (Print) Linking ISSN: 22181989 NLM ISO Abbreviation: Metabolites Subsets: PubMed not MEDLINE
أسماء مطبوعة: Original Publication: Basel : MDPI
مستخلص: 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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فهرسة مساهمة: Keywords: NMR; automatic profiling; machine learning
تواريخ الأحداث: Date Created: 20220421 Latest Revision: 20220716
رمز التحديث: 20231215
مُعرف محوري في PubMed: PMC9027668
DOI: 10.3390/metabo12040283
PMID: 35448470
قاعدة البيانات: MEDLINE
الوصف
تدمد:2218-1989
DOI:10.3390/metabo12040283