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Chris Bizon discussed “Heterogeneity in Obesity Creativity Hub: Transdisciplinary Approaches for Precision Research and Treatment,” and presented data science tools used within the Obesity Hub, a project that aims to take a multidisciplinary and multi-modal approach to understanding obesity. The data science portion of the project began with collecting multi-omic data from a cohort of 10,000 people, including phenotypes, genetic data variations, and measured blood metabolites. The researchers observed the statistical associations between specific factors within the three types of data, and fed their research into the ROBOKOP Knowledge Graph tool, which has a flexible data model that makes it easy to integrate and visualize large amounts of information. The tool then ingested findings from similar research studies, incorporating both the statistical associations recorded by the Obesity Hub researchers with information available about those specific phenotypes, variations, metabolites from previous studies. The “edges” created by the additional information ingested by the graph provides a great amount of mechanistic context as research on the project continues. Click here to view the talk on YouTube.

 

Chris Bizon | Department Bio

Director of Analytics and Data Science, Renaissance Computing Institute (RENCI)

Featured on: May 26, 2022 (Event Page)

Session Title: Improving Health Outcomes (Event Recap

Tools, Information, and Resources:

  • ROBOKOP: Robokop is a biomedical reasoning system that interacts with many biomedical knowledge sources to answer questions. Robokop is one of several prototype systems under active development with NIH NCATS.
  • Obesity Creativity Hub: The Obesity Creativity Hub is a large collaborative project with 27 faculty from 16 departments, six schools, and five centers and institutes focused on understanding why two people who consume the same diets and exercise equally can have very different susceptibility to weight gain, with the aim of developing treatment approaches that go far beyond the “one-size-fits-all” approach that is so common.