Community Shift
PNNL has employed an integrated biomarker approach using a Bayesian statistics framework to assess community shift following an environmental exposure.
The majority of research on biological data integration has focused either on extremely targeted problems, such as protein-protein interactions, or diagnostics (e.g., cancerous or not). For a more generic approach to integration applicable to a wide range of problems, PNNL is developing a Bayesian statistics framework. Building the framework requires four core technical developments:
- an interface for access to disparate sources of data;
- probability mappings of the data to perform Bayesian integration;
- diagnostic models of exposure or response; and
- derivation of biological models for biomarker discovery.
An iterative algorithm identifies the key signatures from each dataset that are improving overall classification. Such a model identifies a subset of potential biomarkers as the most fruitful candidates for continued analysis. In addition, we use both the data at a global scale, and these candidate markers for biological modeling in the demonstration problem spaces.
Contact: Bobbie-Jo Webb-Robertson