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Related Concept Videos

Multiple Comparison Tests01:13

Multiple Comparison Tests

Multiple comparison test, abbreviated as MCT, is a post hoc analysis generally performed after comparing multiple samples with one or more tests. An MCT will help identify a significantly different sample among multiple samples or a factor among multiple factors.
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Sequencing of the human genome has opened up several best-kept secrets of the genome. Scientists have identified thousands of genome variations that exist within a population. These variations can be a single nucleotide or a larger chromosomal variation.
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DNA Microarrays02:34

DNA Microarrays

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Aggregates Classification

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Updated: Jun 5, 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

Cross-platform comparison of microarray-based multiple-class prediction.

Xiaohui Fan1, Li Shao, Hong Fang

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

Plos One
|January 26, 2011
PubMed
Summary
This summary is machine-generated.

Cross-platform microarray analysis is feasible for building diagnostic models. Predictive models trained on one platform can perform well on another, accelerating biomarker translation.

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Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances
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Area of Science:

  • Genomics
  • Bioinformatics
  • Toxicogenomics

Background:

  • High-throughput microarray technology is crucial for biological and medical research.
  • Platform diversity hinders dataset integration and the development of robust diagnostic models.

Purpose of the Study:

  • To evaluate cross-platform consistency for multiple-class prediction using machine learning algorithms.
  • To assess the reusability of predictive features and models across different microarray platforms (Affymetrix and Agilent).

Main Methods:

  • Utilized large toxicogenomics datasets from Affymetrix and Agilent platforms.
  • Applied three widely-used machine learning algorithms for benchmark evaluation.
  • Assessed model performance and feature/model transferability between platforms.

Main Results:

  • Demonstrated successful application of multiple-class prediction models across different commercial microarray platforms.
  • Confirmed that predictive signatures and models trained on one platform can be effectively used on another.
  • Achieved comparable performance when transferring models between platforms.

Conclusions:

  • Cross-platform prediction using microarrays is achievable, facilitating biomarker translation to clinical assays.
  • This study provides a foundation for exploring cross-platform consistency in microarray data analysis.
  • Further research with independent datasets is recommended to confirm feasibility.