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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

1. Coordinated Experiments for High Content Data Collection

   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.

2. Data Management and Analysis

   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.

3. Selection and Validation of Relevant Biomarkers

   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.

4. Understanding Biological Relevance

   Important criteria for selection of environmental biomarkers include relevance to the mode of action and accessiblity of the biomolecules for measurement.

5. Development of High Throughput Sensor Platforms

   Sensor platforms are developed for rapid, economical measurement and biomarker validation.

6. Development of Computational Predictive Models

   Data collected from deployed sensor platforms are used to develop predictive models to support risk management decisions.

IBA Benefits

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.