While complex chronic disease is growing at epidemic proportions in the Western industrialized counties, clinical medicine is generally at a loss stem the tide of this epidemic. Big data, machine learning and artificial intelligence presents great hope in unraveling some of the causes by analyzing connections between specific patterns involving genomics, microbiomics, proteomics, and metabolomics and the development of specific disorders. However, big-data is complicated and involves massive numbers of variables and complex networks. How does a clinician make any sense of this emerging information and leverage it in their clinical approach to complicated patient cases? This presentation will look at some of these issues from a practicing clinician’s perspective and will focus on how to parse through and curate meaningful genomic information (SNPs) utilizing a state-of-the-art medical informatics platform, Opus-23 Explorer, including the deciphering of complex multi-SNP network mappings. The Opus-23 Explorer platform was developed by Dr. Peter D’Adamo at the Center of Excellence in Generative Medicine at the University of Bridgeport and is now being used both by researchers in major academic centers of excellence and by clinicians in private practice settings internationally.
- Participants will be able to discuss the current epidemiologic trends in chronic complex disease and how big-data analytics may help in slowing this epidemic.
- Participants will be able to discuss the linkage & association between the prevalence of various chronic complex diseases and metabolic abnormalities and various genomic patterns in individuals through the lessons learned from population-wide big-data.
- Participants will be able to discuss the available clinical laboratory testing and analysis related to “omics” and how to make big-data work for you as a clinician.
- Participants will be able to discuss the navigation and curation of personal genomic data-sets using the Opus-23 Explorer medical informatics platform and portal.
- Participants will be able to discuss the determination of new therapeutic strategies for various chronic complex diseases and metabolic abnormalities utilizing personalized genomic data-sets and lessons from population-wide big-data analysis.