Integrated Biomarker Approach
A rapid and reliable method for discovery of novel biomarkers through the integration of different biomolecular signature data sets.
IBA - Defining the Steps
IBA steps to biomarker discovery and predictive modeling
Data from coordinated experiments are integrated, combining genomic, proteomic, metabonomic, and lipid data, and novel imaging for controlled and stressed conditions to measure and identify biosignatures.
Statistical and biological workflow and visualization tools are used for data management and analysis to increase the rate of analysis for integrated 'omic data. Discovery of candidate biomarkers using this integrated approach is the first step. Included are probability models for each data source and Bayesian integration of fused results.
The candidate biomarkers combined with additional criteria are used to downselect to a fewer number of biomolecules that become the environmental biomarker(s). These are validated and used to monitor response to environmental stress.
Important criteria for selection of environmental biomarkers include relevance to the mode of action and accessiblity of the biomolecules for measurement.
Sensor platforms are developed for rapid, economical measurement and biomarker validation.
Data collected from deployed sensor platforms are used to develop predictive models to support risk management decisions.
Benefits to using this approach
- Discover novel biomarkers of exposure and response
- Advance our understanding of the biological mechanisms of response to environmental stressors
- Incorporate molecular and imaging analysis to find early indicators of stress
- Integrate multiple levels of information - higher information content increases discovery of new relationships in exposure-response pathways
- Use new analysis and visualization tools to speed feature extraction, ingest probability matrices and view fused classification results
- Identify target pathways for improved validation.