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DNA Microarrays02:34

DNA Microarrays

Microarrays are high-throughput and relatively inexpensive assays that can be automated to analyze large quantities of data at a time. They are used in genome-wide studies to compare gene or protein expression under two varied conditions, such as healthy and diseased states. Microarrays consist of glass or silica slides on which probe molecules are covalently attached through surface functionalization. Most commonly, the slides are prepared through the chemisorption of silanes to silica...

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Analyzing Multifactorial RNA-Seq Experiments with DiCoExpress
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Dynamic-model-based method for selecting significantly expressed genes from time-course expression profiles.

Fang-Xiang Wu1, Wen-Jun Zhang

  • 1Department of Mechanical Engineering, Division of Biomedical Engineering, University of Saskatchewan, Saskatoon, SK S7N5A9, Canada. faw341@mail.usask.ca

IEEE Transactions on Information Technology in Biomedicine : a Publication of the IEEE Engineering in Medicine and Biology Society
|June 17, 2009
PubMed
Summary
This summary is machine-generated.

This study introduces a dynamic model to identify significantly expressed (SE) genes in time-course data. The method uses autoregressive models and Akaike information criterion to distinguish time-dependent gene expression, proving effective in selecting SE genes.

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Area of Science:

  • Bioinformatics
  • Computational Biology
  • Systems Biology

Background:

  • Analyzing time-course gene expression data is crucial for understanding dynamic biological processes.
  • Identifying significantly expressed (SE) genes requires robust statistical methods to distinguish true biological signals from random fluctuations.
  • Existing methods may not adequately capture the temporal dependencies inherent in gene expression profiles over time.

Purpose of the Study:

  • To propose a novel dynamic-model-based method for selecting significantly expressed (SE) genes from time-course expression profiles.
  • To differentiate between time-dependent and time-independent gene expression patterns.
  • To provide a statistically sound approach for identifying genes with meaningful temporal expression changes.

Main Methods:

  • Described time-dependent gene expression using non-zero order autoregressive (AR) models.
  • Modeled time-independent gene expression using zero-order AR models.
  • Employed Akaike information criterion (AIC) to compare AR models and determine profile dependency.

Main Results:

  • The proposed dynamic-model-based method effectively selects significantly expressed (SE) genes.
  • Validation on synthetic and real biological datasets demonstrated the method's accuracy.
  • Performance was assessed using key metrics such as false discovery rate (FDR) and false nondiscovery rate (FNR).

Conclusions:

  • The dynamic-model-based approach is a valid and effective strategy for identifying SE genes in time-course expression data.
  • The method provides a reliable way to distinguish biologically relevant temporal expression patterns.
  • This approach contributes to more accurate analysis of dynamic gene expression studies.