Online Simulation

And More

Top Tags

  1. OMIC analysis
  2. colorectal cancer
  3. biomarker discovery
  4. health services research
  5. screening
  6. mass spectrometry
  7. proteomics
  8. proteome discovery pipeline
  9. statistical models
  10. population-based models
  11. sample acquisition
  12. metabolomics
  13. visual analytics
  14. global proteomics
  15. lipidomics
  16. cceHUB
  17. peptide synthesis
  18. cancer care engineering
  19. diet
  20. peptides
  21. XCT PLUS
  22. tool:workspace
  23. alignment
  24. multi-agent based modeling
  25. cancer care systems

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

Audio podcast
Video podcast

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

  1. OMIC Data Formats: Notes for the Modeling Research Group

    0.0 out of 5 stars 

    20 Feb. 2009 | Notes | Contributor(s): Ann Christine Catlin

    Each OMIC analysis laboratory has one or more analysis workflows that begin with instrument-generated datasets, where the format of the instrument dataset made available to modeling researchers for analysis depends on the instrument, the analysis mode, and the available file conversion software. …

  2. Monte Carlo Simulation for four CRC Incidence Models

    9.9 out of 5 stars 

    12 Dec. 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.

  3. Classification and Alignment of GCxGC-MS Data

    0.0 out of 5 stars 

    02 Dec. 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.

  4. Statistical Modeling of OMIC Data

    0.0 out of 5 stars 

    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 …

  5. Introduction to Population Balance Modeling

    0.0 out of 5 stars 

    02 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 …

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

    0.0 out of 5 stars 

    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 …

  7. Predicting Patient-specific CRC Incidence from Polyp Prevalence

    0.0 out of 5 stars 

    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 …

  8. Colorectal Cancer Incidence Prediction Model

    0.0 out of 5 stars 

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

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

  • Results 1 - 8 of 8
  • Display Num