Online Simulation

And More

Top Tags

  1. colorectal cancer
  2. OMIC analysis
  3. biomarker discovery
  4. screening
  5. health services research
  6. sample acquisition
  7. statistical models
  8. population-based models
  9. visual analytics
  10. lipidomics
  11. peptide synthesis
  12. diet
  13. peptides
  14. metabolomics
  15. cceHUB
  16. protocol
  17. multi-agent based modeling
  18. cancer care engineering
  19. quality improvement
  20. mass spectrometry
  21. cancer care systems
  22. gel electrophoresis
  23. money
  24. dust & magnet
  25. photocleavable linker

Other

Support

Trouble Report

For immediate assistance browse through our support center. You can find answers to many questions in just a few minutes.

If still experiencing problems, send us a report.

required
Why the math question?

Integrative Mathematical Models

Posted 13 Oct, 2008 in Series

0 review(s) (Review this)

0 citations

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.

Cite this work

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

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

    BibTex | EndNote

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

In This Series

Audio podcast
Video podcast

  1. Introduction to Population Balance Modeling

    01 Oct. 2008 | Teaching Materials | Contributor(s): Eric Sherer

    This presentation gives an overview of the role and applications of population balance (or structured) models in describing biological phenomenon. These models represent the continuum between purely empirical models and mechanistic models where details are often lumped into the structured …

  2. A Multi-Agent Approach to Modeling of the Indiana CRC Care System

    19 Jun. 2008 | Online Presentations | Contributor(s): Selen Cremaschi

    This presentation introduces one of the projects from the cancer care engineering portfolio. It focuses on colorectal cancer care system modeling using a multi-agent based approach. The project methodology and its current state are explained. Tapas Das, PhD (USF) Brad Doebbeling, MD, MSc, FACP …

  3. Predicting Patient-specific CRC Incidence from Polyp Prevalence

    17 Jun. 2008 | Teaching Materials | Contributor(s): Eric Sherer

    Seminal work on CRC incidence modeling argued that the slope of the linear log-log CRC incidence with age implies that 6 or 7 somatic mutations are required for transformation to CRC. Subsequent modeling efforts have built on this theme by using models of linear, sequential transformations for …

  4. Colorectal Cancer Incidence Prediction Model

    10 Jun. 2008 | Tools | Contributor(s): Eric Sherer, Mohd Rahmad

    Stochastic simulation of polyp and colorectal cancer (CRC) incidence with patient age.

  5. Statistical Modeling of OMIC Data

    30 Oct. 2008 | Teaching Materials | Contributor(s): Min Zhang

    This presentation describes the statistical modeling work of Professor Min Zhang. A discussion of statistical methods for identifying biomarkers is presented first, including Classical methods and Bayesian and Regularized Variable Selection methods. Next, the alignment of GCxGC MS data using the …

  6. GCxGC MS Data Classification and Alignment

    11 Jun. 2008 | Tools | Contributor(s): Dabao Zhang, Min Zhang, Jason Catlin

    An OMIC data pre-processing tool for the M/Z classification and peak alignment of raw GCxGC-MS data.

  7. Monte Carlo Simulation for four CRC Incidence Models

    16 Jul. 2008 | Tools | Contributor(s): Eric Sherer

    A Monte Carlo Simulator determines the risk of colorectal cancer (CRC) development in a population by calculating multiple, individual patient trajectories.

  • Results 1 - 7 of 7
  • Display Num