Print Email Facebook Twitter MiMIR Title MiMIR: R-shiny application to infer risk factors and endpoints from Nightingale Health's 1H-NMR metabolomics data Author Bizzarri, D. (Leiden University Medical Center) Reinders, M.J.T. (TU Delft Pattern Recognition and Bioinformatics; Leiden University Medical Center) Beekman, M. (Leiden University Medical Center) Slagboom, P. E. (Leiden University Medical Center; Max Planck Institute for Biology of Ageing) van den Akker, E.B. (TU Delft Pattern Recognition and Bioinformatics; Leiden University Medical Center) Date 2022 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. To reference this document use: http://resolver.tudelft.nl/uuid:14e3a9ce-26ad-4e23-8fa2-87cb6a48d288 DOI https://doi.org/10.1093/bioinformatics/btac388 ISSN 1367-4803 Source Bioinformatics, 38 (15), 3847-3849 Part of collection Institutional Repository Document type journal article Rights © 2022 D. Bizzarri, M.J.T. Reinders, M. Beekman, P. E. Slagboom, E.B. van den Akker Files PDF btac388.pdf 618.89 KB Close viewer /islandora/object/uuid:14e3a9ce-26ad-4e23-8fa2-87cb6a48d288/datastream/OBJ/view