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DNA Microarrays

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 15, 2026

In Vivo Modeling of the Morbid Human Genome using Danio rerio

In Vivo Modeling of the Morbid Human Genome using Danio rerio

Published on: August 24, 2013

Impact of missing value imputation on classification for DNA microarray gene expression data--a model-based study.

Youting Sun¹, Ulisses Braga-Neto, Edward R Dougherty

¹Department of Electrical and Computer Engineering, Texas A&M University, College Station, TX 77843, USA.

EURASIP Journal on Bioinformatics & Systems Biology

|March 13, 2010

Summary

This summary is machine-generated.

Missing value imputation in microarray data can improve classification accuracy under certain conditions, especially with high noise or strong correlations. However, imputation is not recommended for large amounts of missing data.

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Area of Science:

Bioinformatics
Computational Biology
Genomics

Background:

Numerous missing-value (MV) imputation methods exist for microarray data.
Few studies have rigorously examined the impact of MV imputation on classification accuracy.
Previous research often suffers from flawed MV generation and error estimation.

Purpose of the Study:

To conduct a model-based investigation into the relationship between MV imputation and classification accuracy in microarray data.
To address fundamental issues identified in prior studies concerning MV handling and evaluation.

Main Methods:

Evaluation of six popular MV imputation algorithms.
Incorporation of two feature selection methods.
Assessment using three distinct classification rules.
Model-based simulation to control for MV generation and error estimation.

Main Results:

MV imputation is beneficial when noise levels are high, variance is low, or gene-cluster correlations are strong, particularly at small to moderate MV rates.
Data quality metrics can guide imputation by identifying poor-quality points for imputation.
Performance of imputation methods exhibits a peaking phenomenon, improving up to an optimal MV rate before deteriorating rapidly.

Conclusions:

MV imputation is recommended for specific data characteristics and moderate missingness rates.
Imputation methods are not advised for high missing value rates.
The optimal MV rate for imputation performance should be considered to avoid accuracy degradation.