Integrative Analysis of Longitudinal Metabolomics Data from a Personal Multi-Omics Profile

Stanberry, Larissa; Mias, George I.; Haynes, Winston; Higdon, Roger; Snyder, M; Kolker, Eugene

doi:10.3390/metabo3030741

Cited by 56 publications

(41 citation statements)

References 60 publications

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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%

Delsa Workshop IV: Launching the Quantified Human Initiative

et al. 2013

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The mission of the Data-Enabled Life Sciences Alliance (DELSA Global) is to ''Accelerate the impact of data-enabled life science research on the pressing needs of the global society.'' In its first 18 months, DELSA has catalyzed connections and interactions for more effective and sustainable science by bringing stakeholders together through physical or virtual proximity to share ideas, discuss new insights, and form novel collaborations.During our most recent annual Washington, DC, meeting (May 16-17, 2013), DELSA brought together life and computer scientists, data analysts, research funding agency representatives, and many others to discuss and formulate plans for furthering the initiative of 21st-century collective innovation. In an exciting day of lightning talks and brainstorming, participants discussed the management and analysis of emerging datasets that hold such immense promise for understanding and improving the human condition and our relationship with the worlds around us and within us.A focus of this meeting was on the Quantified Human (QH) Initiative. QH takes our natural curiosity about self and combines multi-omics and clinical data to draw conclusions about our physical condition both current and future. Measures such as height, weight, and blood pressure have been used throughout medical history; however, it is now possible to track many other measures such as caloric/nutritional intake and output, blood components, and sleep patterns. These data can be viewed in the context of our body as an ecosystem by including measures of the commensal microorganisms, collectively referred to as the microbiome. All of these results, taken together and over a period of time, can lead to a detailed picture of our overall health and open up a whole new level of understanding about the microenvironment that exists inside us. However, the resulting datasets are complex and immense. While the potential exists to use these data to explore the depth and breadth of ourselves in new and unimagined ways, we need new paradigms and policies for organizing, managing, and sharing the data, combined with new publishing and citation models.The QH initiative issues were divided into four categories:Data and Meta-Data, Metrics and Evaluation, Research Outcomes, and Outcomes for the General Public

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Section: Data and Meta-datamentioning

confidence: 99%

Delsa Workshop IV: Launching the Quantified Human Initiative

et al. 2013

Self Cite

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“…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%

–Omic and Electronic Health Record Big Data Analytics for Precision Medicine

Wu¹,

Cheng²,

Kaddi³

et al. 2017

IEEE Trans. Biomed. Eng.

218

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Objective Rapid advances of high-throughput technologies and wide adoption of electronic health records (EHRs) have led to fast accumulation of -omic and EHR data. These voluminous complex data contain abundant information for precision medicine, and big data analytics can extract such knowledge to improve the quality of health care. Methods In this article, we present -omic and EHR data characteristics, associated challenges, and data analytics including data pre-processing, mining, and modeling. Results To demonstrate how big data analytics enables precision medicine, we provide two case studies, including identifying disease biomarkers from multi-omic data and incorporating -omic information into EHR. Conclusion Big data analytics is able to address –omic and EHR data challenges for paradigm shift towards precision medicine. Significance Big data analytics makes sense of –omic and EHR data to improve healthcare outcome. It has long lasting societal impact.

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“…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%

Translational bioinformatics in the era of real-time biomedical, health care and wellness data streams

Shameer¹,

et al. 2016

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Monitoring and modeling biomedical, health care and wellness data from individuals and converging data on a population scale have tremendous potential to improve understanding of the transition to the healthy state of human physiology to disease setting. Wellness monitoring devices and companion software applications capable of generating alerts and sharing data with health care providers or social networks are now available. The accessibility and clinical utility of such data for disease or wellness research are currently limited. Designing methods for streaming data capture, real-time data aggregation, machine learning, predictive analytics and visualization solutions to integrate wellness or health monitoring data elements with the electronic medical records (EMRs) maintained by health care providers permits better utilization. Integration of population-scale biomedical, health care and wellness data would help to stratify patients for active health management and to understand clinically asymptomatic patients and underlying illness trajectories. In this article, we discuss various health-monitoring devices, their ability to capture the unique state of health represented in a patient and their application in individualized diagnostics, prognosis, clinical or wellness intervention. We also discuss examples of translational bioinformatics approaches to integrating patient-generated data with existing EMRs, personal health records, patient portals and clinical data repositories. Briefly, translational bioinformatics methods, tools and resources are at the center of these advances in implementing real-time biomedical and health care analytics in the clinical setting. Furthermore, these advances are poised to play a significant role in clinical decision-making and implementation of data-driven medicine and wellness care.

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Integrative Analysis of Longitudinal Metabolomics Data from a Personal Multi-Omics Profile

Cited by 56 publications

References 60 publications

Delsa Workshop IV: Launching the Quantified Human Initiative

Delsa Workshop IV: Launching the Quantified Human Initiative

–Omic and Electronic Health Record Big Data Analytics for Precision Medicine

Translational bioinformatics in the era of real-time biomedical, health care and wellness data streams

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