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

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Published on: October 11, 2018

Evaluating microarray-based classifiers: an overview.

A-L Boulesteix1, C Strobl, T Augustin

  • 1Sylvia Lawry Centre for MS Research (SLC), Hohenlindenerstr. 1, Munich, Germany.

Cancer Informatics
|March 5, 2009
PubMed
Summary
This summary is machine-generated.

This study reviews statistical methods for evaluating microarray-based class prediction accuracy. It highlights suboptimal procedures and emphasizes best practices for reliable classification in bioinformatics and medicine.

Keywords:
accuracy measuresclassificationconditional and unconditional error rateerror rate estimationgene expressionhigh-dimensional datavalidation datavariable selection

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Area of Science:

  • Bioinformatics
  • Statistics
  • Medical Research

Background:

  • Microarray-based class prediction is widely published in scientific literature.
  • Current assessment of classification accuracy often uses suboptimal statistical procedures.
  • There is a need for rigorous evaluation and validation of predictive models.

Purpose of the Study:

  • To review statistical aspects of classifier evaluation and validation for microarray data.
  • To address practical considerations in assessing classification accuracy.
  • To guide researchers in applying appropriate statistical methodologies.

Main Methods:

  • Review of statistical literature on classifier evaluation.
  • Discussion of accuracy measures and error rate estimation.
  • Analysis of variable selection, classifier choice, and validation strategies.

Main Results:

  • Identified suboptimal practices in current classification accuracy assessment.
  • Detailed review of key statistical considerations for reliable evaluation.
  • Provided practical guidance on validation strategies.

Conclusions:

  • Rigorous statistical evaluation is crucial for accurate microarray-based class prediction.
  • Adopting recommended practices enhances the reliability of bioinformatics and medical research findings.
  • Standardized validation improves the translational potential of predictive models.