In the near future, the state of an individual’s health and well-being will be characterized with increasing precision from the molecular level to the organ-system level to the population level and will incorporate data modalities that assess the effects of behavior, therapeutics, nutrients, the microbiome and the environment on the individual. The size and complexity of high-dimensional characterization of patients will lead to far more complex diagnostic and prognostic categories than are currently in use, requiring that information infrastructure for medical decision-making be made accessible to the individual patient and to entire populations. The multivariate descriptors of large populations will allow stratification of kinds only seen in the most recent genomically informed clinical trials. Complex, but empirically validated, algorithms will be embedded in electronic health record systems as decision-support tools to assist in everyday patient care. Those management algorithms will evolve and be modified continuously based on inputs from ongoing clinical experience and from new research. Our research programs have evolved to reflect the full breadth of this spectrum and can best be captured as follows.
Provisioning big data to the scientific community
Getting the big picture on human health
What is the true nature of human disease? The critical element for precision medicine is aligning all data from both conventional medical and alternative data sources (environmental monitoring, social platforms, wearables, genomics, epigenomics, etc.).
Example: PIC-SURE, a BD2K Center of Excellence
Learning from each patient
How do we study and help patients with rare or unique diseases where the standard epidemiological tools and healthcare systems are not effective?
Example: Undiagnosed Diseases Network (UDN) Coordinating Center
Advancing basic science with data science
With the sequence of the human genomes on the way to being elaborated, how do these sequences orchestrate multicellular life and its interaction with the environment?
Example: 4DNucleome (4DN) Data Coordination and Integration Center
Understanding disease beyond heredity: environmental impact
Most common diseases have more than half of their contributing risk arising from the environment. Can we use ’omic techniques to identify and modify these risks?
- A database of human exposomes and phenomes from the US National Health and Nutrition Examination Survey
- Systematic correlation of environmental exposure and physiological and self-reported behaviour factors with leukocyte telomere length
Instrumenting the health enterprise for discovery and intervention
What technologies can we develop to harvest the wealth of information contained in Big Data, enabling change in biomedical discovery and care at the national and international scale?
Example: Accessible Research Commons for Health (ARCH)