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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: Apr 12, 2026

Microarray Analysis for Saccharomyces cerevisiae

Microarray Analysis for Saccharomyces cerevisiae

Published on: April 7, 2011

Ontology-Based Analysis of Microarray Data.

Agapito Giuseppe¹, Marianna Milano²

¹Department of Surgical and Medical Sciences, University of Catanzaro, Viale Europa-Localita Germaneto, Catanzaro, 88100, Italy. agapito@unicz.it.

Methods in Molecular Biology (Clifton, N.J.)

|May 15, 2015

Summary

This summary is machine-generated.

Semantic methods enhance biological data analysis, particularly for protein interaction networks and microarray data. Integrating biological ontologies with computational algorithms improves understanding of complex biological processes and diseases.

Keywords:

Bi-clustering Data mining Expression patterns Microarray

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Bacterial Gene Expression Analysis Using Microarrays

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Area of Science:

Bioinformatics
Computational Biology
Systems Biology

Background:

Biological data analysis is increasingly reliant on semantic-based methods.
Biological ontologies provide accurate and comprehensive knowledge for computational analysis.
Recent algorithms effectively leverage ontological knowledge for biological data interpretation.

Purpose of the Study:

To focus on semantic-based management and analysis of protein interaction networks.
To explore the use of knowledge encoded in biological ontologies for protein-protein interaction data analysis.
To investigate the application of semantic approaches to microarray data for disease understanding.

Main Methods:

Utilizing knowledge from biological ontologies.
Applying semantic-based techniques to protein-protein interaction network data.
Integrating semantic approaches with high-throughput data analysis, including genomic and expression data.

Main Results:

Semantic methods are valuable for analyzing protein interaction networks.
The integration of ontologies enhances the analysis of complex biological data.
These approaches can broaden the application of computational methods in biology.

Conclusions:

Semantic-based management and analysis are crucial for biological data.
Ontology-driven computational approaches advance the study of molecular machineries.
Application to microarray data can improve understanding of disease development.