MiMIR: R-shiny application to infer risk factors and endpoints from Nightingale Health's 1H-NMR metabolomics data

D. Bizzarri, M.J.T. Reinders, M. Beekman, P. E. Slagboom, E.B. van den Akker*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

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Abstract

Motivation: 1H-NMR metabolomics is rapidly becoming a standard resource in large epidemiological studies to acquire metabolic profiles in large numbers of samples in a relatively low-priced and standardized manner. Concomitantly, metabolomics-based models are increasingly developed that capture disease risk or clinical risk factors. These developments raise the need for user-friendly toolbox to inspect new 1H-NMR metabolomics data and project a wide array of previously established risk models. Results: We present MiMIR (Metabolomics-based Models for Imputing Risk), a graphical user interface that provides an intuitive framework for ad hoc statistical analysis of Nightingale Health's 1H-NMR metabolomics data and allows for the projection and calibration of 24 pre-trained metabolomics-based models, without any pre-required programming knowledge.

Original languageEnglish
Pages (from-to)3847-3849
Number of pages3
JournalBioinformatics
Volume38
Issue number15
DOIs
Publication statusPublished - 2022

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