MiMIR

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

Journal article (2022)

Authors

D. Bizzarri Leiden University Medical Center
M.J.T. Reinders Pattern Recognition and Bioinformatics - EEMCS , Leiden University Medical Center
M. Beekman Leiden University Medical Center
P. Eline Slagboom Leiden University Medical Center, Max Planck Institute for Biology of Ageing
E.B. van den Akker Pattern Recognition and Bioinformatics - EEMCS , Leiden University Medical Center
Research Group
Pattern Recognition and Bioinformatics (EEMCS) (TU Delft)
Copyright: © 2022 D. Bizzarri, M.J.T. Reinders, M. Beekman, P. E. Slagboom, E.B. van den Akker
DOI: https://doi.org/10.1093/bioinformatics/btac388
More Info
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Published Date

2022

Language

English

Faculty

Electrical Engineering, Mathematics and Computer Science

Department

Intelligent Systems

Research Group

Pattern Recognition and Bioinformatics

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.