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DNA Microarrays

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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

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances

Published on: October 11, 2018

Multiclass classification of microarray data samples with a reduced number of genes.

Elizabeth Tapia¹, Leonardo Ornella, Pilar Bulacio

¹CIFASIS-Conicet Institute, Bv, 27 de Febrero 210 Bis, Rosario, Argentina. tapia@cifasis-conicet.gov.ar

BMC Bioinformatics

|February 24, 2011

Summary

This study introduces a new bound for gene selection in multiclass classification of microarray data. This bound helps create accurate and computationally efficient classification models.

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Area of Science:

Bioinformatics
Computational Biology
Genomics

Background:

Multiclass classification of microarray data is challenging, especially with increased classes and reliance on gene selection.
Current methods lack estimates for the maximum number of genes, leading to computational inefficiency or biased models.

Purpose of the Study:

To present a novel bound for the maximum number of genes in binary classifiers for multiclass microarray data classification.
To explore the utility of this bound in developing accurate and sparse classification models.

Main Methods:

Development of a novel theoretical bound for gene selection in binary mediated multiclass classification algorithms.
Application of the bound to guide the selection of gene sets for microarray data classification.

Main Results:

A new bound was derived for the maximum number of genes usable by binary classifiers in multiclass settings.
The bound indicates that high-dimensional binary output domains can support accurate and sparse multiclass classifiers for microarray data.

Conclusions:

Experimental validation confirmed the bound's effectiveness in developing accurate and sparse multiclass classifiers.
The proposed bound is a valuable tool for optimizing gene selection in bioinformatics research.