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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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Related Experiment Video

Updated: Jun 10, 2026

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances
07:35

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances

Published on: October 11, 2018

Consistency of predictive signature genes and classifiers generated using different microarray platforms.

X Fan1, E K Lobenhofer, M Chen

  • 1College of Pharmaceutical Sciences, Zhejiang University, Hangzhou, China.

The Pharmacogenomics Journal
|August 3, 2010
PubMed
Summary
This summary is machine-generated.

Microarray gene classifiers show high cross-platform prediction consistency. Published signature genes and classifiers can be reliably used across different platforms for toxicogenomics applications.

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Last Updated: Jun 10, 2026

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DNA Microarrays: Sample Quality Control, Array Hybridization and Scanning
09:27

DNA Microarrays: Sample Quality Control, Array Hybridization and Scanning

Published on: March 15, 2011

Area of Science:

  • Genomics
  • Toxicogenomics
  • Biomarker Discovery

Background:

  • Microarray-based gene classifiers and signature genes are widely reported.
  • Their utility in cross-platform prediction remains largely uncertain.

Purpose of the Study:

  • To assess the cross-platform prediction consistency of microarray-based classifiers and signature genes.
  • To evaluate the utility of published classifiers and genes in diverse applications.

Main Methods:

  • Utilized a large toxicogenomics dataset from the MicroArray Quality Control Phase II (MAQC-II) project.
  • Applied signature genes from one platform to develop classifiers on another.
  • Validated classifiers developed on one platform using data from a different platform.

Main Results:

  • Achieved 80-90% cross-platform prediction consistency.
  • Demonstrated that signature genes from one platform can be directly applied to another.
  • Showed that classifiers developed on one platform accurately predict samples from different platforms.

Conclusions:

  • Published microarray signature genes and classifiers exhibit significant utility in cross-platform applications.
  • Results support the adoption of published classifiers and the translation of microarray-identified biomarkers to non-array assays.