2013
DOI: 10.3390/metabo3030741
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Integrative Analysis of Longitudinal Metabolomics Data from a Personal Multi-Omics Profile

Abstract: The integrative personal omics profile (iPOP) is a pioneering study that combines genomics, transcriptomics, proteomics, metabolomics and autoantibody profiles from a single individual over a 14-month period. The observation period includes two episodes of viral infection: a human rhinovirus and a respiratory syncytial virus. The profile studies give an informative snapshot into the biological functioning of an organism. We hypothesize that pathway expression levels are associated with disease status. To test … Show more

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Cited by 56 publications
(41 citation statements)
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“…There are currently two landmark datasets available for exploration, the Dr. Larry Smarr personal multi-omics data 1 and the integrative personal multi-omics profile. 2,3 We were extremely fortunate to have Dr. Larry Smarr as our keynote speaker. To have the person responsible for one of the QH landmark datasets discuss the impact this science has on his health was a powerful call to action.…”
Section: Data and Meta-datamentioning
confidence: 99%
“…There are currently two landmark datasets available for exploration, the Dr. Larry Smarr personal multi-omics data 1 and the integrative personal multi-omics profile. 2,3 We were extremely fortunate to have Dr. Larry Smarr as our keynote speaker. To have the person responsible for one of the QH landmark datasets discuss the impact this science has on his health was a powerful call to action.…”
Section: Data and Meta-datamentioning
confidence: 99%
“…For example, the integrative personal omics profile (iPOP) project has integrated multiple molecular expression profiles to uncover dynamic molecular changes between healthy and diseased states [52]. However, integrating multi-omic data is challenging because of variations in represented biological processes, technical and biological noise levels, identification accuracy, spatiotemporal resolution, and many other confounding factors [53]. In EHR, the data are inherently heterogeneous.…”
Section: Big Data For Precision Medicinementioning
confidence: 99%
“…The first attempt for the clinical interpretation of whole genome showed that the index patient is at increased risk of cardiovascular disease that were not typically illustrative with existing risk prediction models [168] and the compendium of variants implicated in rare, common or orphan diseases are growing at a rapid pace. Temporal profiling of a single patient for 14 days using biological (genomics, proteomics, metabolomics and transcriptomics) and clinical phenotypes have revealed how longitudinal measurements of multiscale biological data showed the dynamics of biological pathways during illness and wellness [169, 170]. ClinicalTrials.gov lists 113 studies that use wearable devices (https://clinicaltrials.gov/ct2/results?term=%22wearable%22).…”
Section: Role Of Translational Bioinformatics In Personalized Biomedimentioning
confidence: 99%