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Related Concept Videos

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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Evaluating the information management and collaboration of "bench to bedside" microarray research computing with

Nicholas Anderson1

  • 1University of Washington, Seattle, Washington, USA.

AMIA ... Annual Symposium Proceedings. AMIA Symposium
|January 24, 2007
PubMed
Summary

This study examines how laboratories manage complex gene expression data from microarray experiments. It identifies key factors influencing successful information management and collaboration in translational research.

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Area of Science:

  • Genomics
  • Bioinformatics
  • Translational Research

Background:

  • Microarray gene expression experiments generate complex, data-intensive information.
  • Effective information management and collaboration are crucial for basic science and clinical research.

Purpose of the Study:

  • To qualitatively evaluate information management, collaboration, and analysis in microarray gene expression research.
  • To explore information use in laboratories conducting or having completed translational microarray research.

Main Methods:

  • Qualitative longitudinal evaluation using direct observation and in-depth interviews.
  • Theoretical framework guiding exploration of information use.
  • Case analysis to identify factors influencing information management.

Main Results:

  • Identified positive and negative factors associated with managing complex data from microarray experiments.
  • Contrasted information use patterns in different laboratory settings.
  • Highlighted challenges and best practices in laboratory information management.

Conclusions:

  • Understanding information management is key to optimizing microarray research workflows.
  • Translational research benefits from robust data handling and collaborative practices.
  • Case analysis provides valuable insights into laboratory-specific information management strategies.