Automated metabolic assignment: Semi-supervised learning in metabolic analysis employing two dimensional Nuclear Magnetic Resonance (NMR)

Migdadi, Lubaba; Lambert, Jörg; Telfah, Ahmad; Hergenröder, Roland; Wöhler, Christian

doi:10.1016/j.csbj.2021.08.048

Cited by 8 publications

(12 citation statements)

References 49 publications

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“…Furthermore, the kynurenine pathway was previously associated with the elevated levels of cytokines [53], which is consistent with the results of our previous study [12,13]. A decreased concentration of tryptophan was also observed in rat blood serum after exposure to TiO 2 NPs [52], which is in agreement with the findings in workers exposed to nanoTiO 2 , and similar oxidative stress effects [54,55].…”

Section: Analysis Of Blood Plasmasupporting

confidence: 92%

“…Such an observation is complementary to the findings of a previous study performed on the same cohort, where the markers of nucleic acids' oxidation (8-hydroxyguanosine and 8-hydroxy-2-deoxyguanosine) were identified in pre-shift EBC samples. Similar markers were also found in other toxicological studies of occupational exposure to different NPs, indicating a general effect of chronic NPs exposure [12,54,55].…”

Section: Analysis Of Blood Plasmasupporting

confidence: 85%

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Research in Metabolomics via Nuclear Magnetic Resonance Spectroscopy: Data Mining, Biochemistry and Clinical Chemistry

2022

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Section: Analysis Of Blood Plasmasupporting

confidence: 92%

Section: Analysis Of Blood Plasmasupporting

confidence: 85%

Research in Metabolomics via Nuclear Magnetic Resonance Spectroscopy: Data Mining, Biochemistry and Clinical Chemistry

2022

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“…While this systematic review aims to provide an update on current methods commonly employed in the NMR metabolomics workflow and point out the extent of their variation between studies, we still expect to see variation in the future as improvements are made to existing methods or new methodologies are developed. The necessity for standardisation is often debated as there are continued advancements made in the NMR metabolomics field such as sample collection devices [ 235 , 236 ], data processing software [ 237 ], NMR experiments and pulse sequences [ 238 ], improved instrument sensitivity [ 239 , 240 ], and metabolite deconvolution software [ 241 , 242 , 243 ]. Therefore, when using new methods, we encourage careful considerations in their implementation and detailed reporting.…”

Section: Discussionmentioning

confidence: 99%

Systematic Review of NMR-Based Metabolomics Practices in Human Disease Research

et al. 2022

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Nuclear magnetic resonance (NMR) spectroscopy is one of the principal analytical techniques for metabolomics. It has the advantages of minimal sample preparation and high reproducibility, making it an ideal technique for generating large amounts of metabolomics data for biobanks and large-scale studies. Metabolomics is a popular “omics” technology and has established itself as a comprehensive exploratory biomarker tool; however, it has yet to reach its collaborative potential in data collation due to the lack of standardisation of the metabolomics workflow seen across small-scale studies. This systematic review compiles the different NMR metabolomics methods used for serum, plasma, and urine studies, from sample collection to data analysis, that were most popularly employed over a two-year period in 2019 and 2020. It also outlines how these methods influence the raw data and the downstream interpretations, and the importance of reporting for reproducibility and result validation. This review can act as a valuable summary of NMR metabolomic workflows that are actively used in human biofluid research and will help guide the workflow choice for future research.

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“…We distinguish between classification or regression problems, depending on whether the variables are discrete or continuous, respectively. The supervised technique is based on the association between the response variable (used to drive the model training) and the predictors (namely covariates) with the aim to perform precise predictions [53][54][55]. In fact, first, a training dataset is used as fitting model, while, in a second step, a testing dataset is used to estimate the predictive power.…”

Section: Supervised Methodsmentioning

confidence: 99%

NMR in Metabolomics: From Conventional Statistics to Machine Learning and Neural Network Approaches

et al. 2022

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NMR measurements combined with chemometrics allow achieving a great amount of information for the identification of potential biomarkers responsible for a precise metabolic pathway. These kinds of data are useful in different fields, ranging from food to biomedical fields, including health science. The investigation of the whole set of metabolites in a sample, representing its fingerprint in the considered condition, is known as metabolomics and may take advantage of different statistical tools. The new frontier is to adopt self-learning techniques to enhance clustering or classification actions that can improve the predictive power over large amounts of data. Although machine learning is already employed in metabolomics, deep learning and artificial neural networks approaches were only recently successfully applied. In this work, we give an overview of the statistical approaches underlying the wide range of opportunities that machine learning and neural networks allow to perform with accurate metabolites assignment and quantification.Various actual challenges are discussed, such as proper metabolomics, deep learning architectures and model accuracy.

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Automated metabolic assignment: Semi-supervised learning in metabolic analysis employing two dimensional Nuclear Magnetic Resonance (NMR)

Abstract: Graphical abstract

Cited by 8 publications

References 49 publications

Research in Metabolomics via Nuclear Magnetic Resonance Spectroscopy: Data Mining, Biochemistry and Clinical Chemistry

Research in Metabolomics via Nuclear Magnetic Resonance Spectroscopy: Data Mining, Biochemistry and Clinical Chemistry

Systematic Review of NMR-Based Metabolomics Practices in Human Disease Research

NMR in Metabolomics: From Conventional Statistics to Machine Learning and Neural Network Approaches

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