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Integrative Mathematical Models

Posted 13 Oct, 2008 in Series

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Abstract

Statistical, Population-based and Agent-based Modeling
Data from the four OMIC analyses will be transferred to the statistical modelers for integration and analysis. Integrative statistical modeling is the core of the clinical aspects of the CCE project. The goal is to develop and implement a data analysis framework that will effectively integrate and explore the vast array of patient data, which include the OMIC analyses, clinical treatment regimens and outcomes, and demographic data. Because of the enormous size of the OMIC databases, this framework will support

  • a data repository and data navigation capability tailored for access and integration by exploratory methods that lead to conjectured models
  • methods of model diagnostics that together with cancer domain knowledge reject or accept these conjectured models
  • iterative exploration and modeling until valid models are discovered and, as a result
  • statistical methods of estimation and testing based on the validated models.

image

The OMIC infrastructure supports a knowledge discovery environment that provides off-the-shelf analysis, as well as the capability to "program with the data" -- allowing wholly new methods of analysis to be readily developed that are tailored to the data. Cutting across the OMIC data and modeling effort will be tools and methods for data visualization, a critical technology for understanding very large amounts of information.

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If you reference this work in a publication, please cite as follows:

  • (2008), "Integrative Mathematical Models," http://ccehub.org/resources/104.

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Tags
  1. biomarker discovery
  2. multi-agent based modeling
  3. OMIC analysis
  4. population-based models
  5. statistical models

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