Objective 7: Leverage emerging opportunities in data science to open new frontiers in HLBS research

New technologies, from “omics” platforms to high-throughput screening, have generated vast amounts of data that have the potential to provide new insights into the preemption and precise treatment of  heart, lung, blood, and sleep (HLBS) disorders. Unfortunately, only a small portion of this data is being optimally assessed and incorporated into practice. Developing innovative approaches to the integration, analysis, and interpretation of data from multiple sources will be essential. This information can then be effectively used to understand biological, social, and behavioral determinants associated with HLBS health and disease and to improve patient outcomes.

Envision a future in which we are able to...
  • Empower heart failure patients and their health care providers to co-manage care by using smartphone apps with personalized treatment algorithms that integrate electronic health record data, medication use monitors, and personal sensor data (e.g., body weight, heart rhythm, fluid balance) to sustain optimal function and quality of life.
  • Accelerate the discovery of mediators of health and disease through the seamless and shared use of readily accessible cloud-based datasets that integrate genomics and other omics analyses with carefully phenotyped research participant data from diverse sources.
  • Develop handheld devices to process and integrate genomics, clinical, and personal health data at the bedside to assist clinicians in their care of patients.

Related Priorities

Compelling Question
How do we encourage training in biostatistics, computer science, and bioinformatics to reach the entire biomedical community in this era of very large data sets? (7.CQ.01)
Critical Challenge
The development, application, and sharing of robust and multidimensional data-analytical and theoretical methods, mathematical modeling, and computational simulation techniques are needed for understanding fundamental mechanisms of HLBS systems, including gene, protein, and metabolic regulatory networks and the impact of environmental exposures on those networks. (7.CC.01)
Critical Challenge
Novel integrative systems biology and analytical approaches are needed to exploit the wealth of knowledge coming from genetics, epigenetics, transcriptomics, metabolomics, proteomics, environmental exposures, electronic health records, and imaging to define disease subtypes, predict risks, and identify therapeutic targets. (7.CC.02)
Critical Challenge
Novel analytical approaches, coordinated access to data, well-planned sample analyses, and creation of a scientific data commons are needed to leverage existing deeply phenotyped cohorts to accelerate translational research and promote the discovery of key druggable targets and the development of novel and precise treatments for HLBS diseases. (7.CC.03)
Critical Challenge
Advancements are needed in the organization, infrastructure, integration, and availability of "omics" data, including genetic, epigenetic, transcriptomic, metabolomic, proteomic, phenotypic, and ontologic information. (7.CC.04)