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bioRxiv · 10.1101/2021.12.08.471816

Temporal response characterization across individual multiomics profiles of prediabetic and diabetic subjects

Zheng, M.·Piermarocchi, C.·Mias, G. I.
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Abstract

Longitudinal deep multi-omics profiling, which combines biomolecular, physiological, environmental and clinical measures data, shows great promise for precision health. However, integrating and understanding the complexity of such data remains a big challenge. Here we propose a bottom-up framework starting from assessing single individuals multi-omics time series, and using individual responses to assess multi-individual grouping based directly on similarity of their longitudinal deep multi-omics profiles. We applied our method to individual profiles from a study profiling longitudinal responses in type 2 diabetes mellitus. After generating periodograms for individual subject omics signals, we constructed within-person omics networks and analyzed personal-level immune changes. The results showed that our method identified both individual-level responses to immune perturbation, and the clusters of individuals that have similar behaviors in immune response and which was associated to measures of their diabetic status.

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Zheng, M., Piermarocchi, C., Mias, G. I.. 2021-12-10. Temporal response characterization across individual multiomics profiles of prediabetic and diabetic subjects. https://doi.org/10.1101/2021.12.08.471816

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