MiMIR
R-shiny application to infer risk factors and endpoints from Nightingale Health's 1H-NMR metabolomics data
D. Bizzarri (Leiden University Medical Center)
Marcel J.T. Reinders (TU Delft - Pattern Recognition and Bioinformatics, Leiden University Medical Center)
M Beekman (Leiden University Medical Center)
P.E. Slagboom (Leiden University Medical Center, Max Planck Institute for Biology of Ageing)
Erik van den Akker (TU Delft - Pattern Recognition and Bioinformatics, Leiden University Medical Center)
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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.
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