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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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Performing Data Mining And Integrative Analysis Of Biomarker in Breast Cancer Using Multiple Publicly Accessible Databases
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Interpreting microarray data: towards the complete bioinformatics toolkit for cancer.

Michael L Roberts1, Stavros D Kottaridis

  • 1Regulon A.E., Afxentiou 7, Athens 11525, Greece. michael@regulon.org

Cancer Genomics & Proteomics
|September 20, 2007
PubMed
Summary
This summary is machine-generated.

Functional genomics and bioinformatics analyze gene expression data from microarrays to understand cancer progression. This approach helps identify transcription regulatory networks in tumors for improved diagnosis and targeted therapies.

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Area of Science:

  • Genomics
  • Bioinformatics
  • Cancer Research

Background:

  • Functional genomics and microarray analysis have been used for nearly a decade to study human malignancies.
  • These techniques aim to re-classify carcinomas, aid in diagnosis/prognosis, and predict treatment responses.

Purpose of the Study:

  • To review advances in functional genomics for interpreting microarray data.
  • To highlight methods for revealing transcription regulatory networks in cancer cells.

Main Methods:

  • Utilizing bioinformatics tools to mine large datasets from genome-wide analysis.
  • Employing techniques to identify active transcription regulatory networks in response to disease states.

Main Results:

  • Microarray analysis aids in molecular classification and prognosis of carcinomas.
  • Bioinformatics tools are crucial for interpreting complex genomic data.

Conclusions:

  • Deciphering transcription networks in malignant cells enhances understanding of cancer progression.
  • Identifying aberrant networks offers potential for developing novel therapeutic strategies.