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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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Updated: Jun 27, 2026

Characterization of Functionally Associated miRNAs in Glioblastoma and their Engineering into Artificial Clusters for Gene Therapy

Characterization of Functionally Associated miRNAs in Glioblastoma and their Engineering into Artificial Clusters for Gene Therapy

Published on: October 4, 2019

Fast gene ontology based clustering for microarray experiments.

Kristian Ovaska¹, Marko Laakso, Sampsa Hautaniemi

¹Computational Systems Biology Laboratory, Institute of Biomedicine and Genome-Scale Biology Program, Biomedicum Helsinki, University of Helsinki, Finland.

|November 26, 2008

Summary

This summary is machine-generated.

This study introduces fast software for advanced gene annotation using semantic similarity and clustering. It enables rapid identification of genes with shared Gene Ontology functions and improves biological hypothesis generation.

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Area of Science:

Bioinformatics
Computational Biology
Genomics

Background:

Microarray analysis frequently yields numerous disease-associated genes.
Gene Ontology (GO) annotations are used to identify biological processes but can result in overwhelming numbers of altered processes.
Interpreting GO results and generating novel hypotheses can be challenging.

Purpose of the Study:

To develop fast software for advanced gene annotation.
To leverage semantic similarity for Gene Ontology terms combined with clustering and heatmap visualization.
To facilitate the identification of genes sharing common biological processes and functions.

Main Methods:

Utilized semantic similarity for Gene Ontology terms.
Implemented clustering and heatmap visualization techniques.
Developed an R-based open-source package for gene annotation.

Main Results:

Achieved a speed advantage of over 2000-fold compared to existing implementations.
Enabled rapid identification of genes within the same Gene Ontology cluster.
Facilitated the visualization of gene expression patterns through heatmaps.

Conclusions:

The developed package offers advanced gene annotation capabilities.
Hierarchical clustering dendrograms aid in identifying genes with shared GO terms.
The methods support the interpretation of complex gene analysis results and hypothesis generation.