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OS02 - Emerging Technologies - Block Chain / Genomics (ID 3)
- Event: e-Health 2018 Virtual Meeting
- Type: Oral Session
- Track: Clinical Delivery
- Presentations: 1
- Coordinates: 5/28/2018, 11:30 AM - 12:30 PM, Fairview V Room, Conference Level
OS02.04 - Multi-Omic Analysis Revolutionizing Personalized Health (ID 35)
Purpose/Objectives: Healthcare is undergoing a revolution as omics analyses have become increasingly affordable and accessible to evaluate risk of disease, diagnose disease, match treatment to disease and to enable preventive medicine. These analyses include genomics to detect genetic diseases and ascertain disease risk, and proteomic, metabolomic and exposomic to determine the environmental impact in disease development and to monitor response to treatment. Further, microbiomic analysis can give insight into the origins of immune disorders and other diseases. What is required is technology that brings together a full suite of omic tests, bioinformatics and systems biology expertise and big data analytics to deliver personalized health insights and action plans in an all-in-one online platform.
Methodology/Approach: With the use of big data analytics and artificial intelligence tools, individual data clouds of genomic, proteomic, metabolomic, microbiomic, exposomic and phenomic profiles are generated that can precisely characterize the presence and potential of disease, match treatments and monitor outcomes. The unique datasets of each individual can be formulated and referenced against curated literature to produce personalized health insights and custom action plans. Machine-learning tools can be utilized to suggest a customized action plan for individuals, assembled together with their health practitioners, that target actionable results from the molecular analysis. To provide valuable longitudinal data, tracking tools can be used to track an individuals performance over time, capturing valuable live data insights and motivating individuals to re-test to evaluate their progress. With the live reporting of actions connected to molecular measures in the action plan, the machine-learning data centre will capture behaviors and the effects of treatment interventions.
Finding/Results: A pharmacogenomics study indicated that 97% of the population studied has a variant in their genetic code that would change the dose or medication for the most common health conditions. The value of multi-omic analyses has been demonstrated in a 108 person study applied to healthy individuals. The results indicated that every person in the study had an actionable possibility for better health. The power of integrating multi-omic analyses is evident when looking at complex diseases where unique bio-signatures emerge and the co-morbidities are presented even as they are emerging. Generally our health status determinants are 30% genetics, 60% behaviour and environmental and 10% health care.
Conclusion/Implications/Recommendations: Any single omic analysis is limited in its scope, making it necessary for an integration of a maximal number of omic analyses to provide information concerning diseases you have or are trending towards. Multi-omic analysis supports personalized healthcare and allows individuals to understand their bodys unique characteristics, its sensitivities and how it best responds. With this foundation of knowledge, an individual can make informed health decisions. Data analytics with artificial intelligence systems will provide novel insights not previously imagined. Benefits could include identification of novel biomarkers associated with disease and the precursors of disease. The use of multi-omic analyses will shift our current delivery of the reactive sick-care model to a truly preventive, personalized health care model.
140 Character Summary: Implement advances in multi-omic methods, computational analysis, data visualization and design for personalized health solutions.
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