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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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Optimization for Sequencing and Analysis of Degraded FFPE-RNA Samples

Optimization for Sequencing and Analysis of Degraded FFPE-RNA Samples

Published on: June 8, 2020

A review of feature extraction software for microarray gene expression data.

Ching Siang Tan¹, Wai Soon Ting¹, Mohd Saberi Mohamad¹

¹Artificial Intelligence and Bioinformatics Research Group, Faculty of Computing, Universiti Teknologi Malaysia, 81310 Skudai, Johor, Malaysia.

Biomed Research International

|September 25, 2014

Summary

This summary is machine-generated.

This study reviews software for feature extraction, a method to reduce large gene expression datasets. Carefully selected gene sets enable efficient data analysis by retaining relevant information.

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Area of Science:

Bioinformatics
Computational Biology
Genomics

Background:

Large-scale gene expression data present computational challenges.
Feature extraction reduces data dimensionality while preserving essential information.
This process is crucial for downstream biological analysis.

Purpose of the Study:

To review software applications for gene expression data feature extraction.
To provide a comprehensive overview of available tools for dimensionality reduction in genomics.

Main Methods:

The review focuses on feature extraction techniques including Principal Component Analysis (PCA), Independent Component Analysis (ICA), Partial Least Squares (PLS), and Local Linear Embedding (LLE).
Software applications for these methods were identified and evaluated.

Main Results:

Numerous software applications for PCA, ICA, PLS, and LLE are available.
A summary and sources for each software type are provided.

Conclusions:

Effective feature extraction is vital for analyzing large gene expression datasets.
The reviewed software facilitates the application of dimensionality reduction techniques in genomics research.