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Correlated Discretized Expression score: a method for identifying gene interaction networks from time course

Peter Larsen1, Eyad Almasri, Guanrao Chen

  • 1Core Genomics Lab., Illinois Univ., Chicago, IL 60607-7058, USA. plarsen@uic.edu

Conference Proceedings : ... Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual Conference
|October 20, 2007
PubMed
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This study introduces a new method using Correlated Discretized Expression (CDE) scores to identify gene interaction networks from time course microarray data, revealing causal relationships in gene regulation.

Area of Science:

  • Genomics
  • Systems Biology
  • Bioinformatics

Background:

  • Gene interaction network construction is crucial for understanding cellular processes.
  • Time course microarray data offers insights into dynamic gene regulation.
  • Identifying causal relationships between regulators and targets remains challenging.

Purpose of the Study:

  • To develop a novel system for identifying gene regulatory patterns from time course microarray data.
  • To propose a method for constructing gene interaction networks by analyzing temporal expression changes.
  • To assess the effectiveness of the proposed system in uncovering gene expression dynamics.

Main Methods:

  • The study utilizes Correlated Discretized Expression (CDE) scores.
  • Microarray data is discretized to highlight significant gene expression changes.

Related Experiment Videos

  • Biological expectations of regulatory interactions inform pattern identification.
  • Main Results:

    • The Correlated Discretized Expression (CDE) score effectively identifies significant gene expression changes.
    • The proposed system successfully demonstrates the identification of gene expression patterns.
    • Analysis of hypothetical and S. cerevisiae cell cycle data validates the method's utility.

    Conclusions:

    • The Correlated Discretized Expression (CDE) score provides a robust method for analyzing time course microarray data.
    • This approach facilitates the construction of gene interaction networks and the understanding of regulatory dynamics.
    • The system is effective for both simulated and real biological datasets, including the S. cerevisiae cell cycle.