MiMIR
R-shiny application to infer risk factors and endpoints from Nightingale Health's 1H-NMR metabolomics data
Daniele Bizzarri (Leiden University Medical Center)
M.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 Ben 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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