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Identification of disease-causing genes using microarray data mining and Gene Ontology.

Azadeh Mohammadi¹, Mohammad H Saraee, Mansoor Salehi

¹Intelligent Databases, Data mining and Bioinformatics Laboratory, Isfahan University of Technology, Isfahan, Iran. amohammadi@ec.iut.ac.ir

BMC Medical Genomics

|January 29, 2011

Summary

This study introduces a new gene selection framework combining Fisher and Support Vector Machine-Recursive Feature Elimination (SVM-RFE) methods. The approach enhances cancer marker gene identification by reducing redundancy and incorporating Gene Ontology data for improved accuracy.

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Area of Science:

Bioinformatics
Computational Biology
Genomics

Background:

Microarray technology is crucial for identifying disease-causing genes through expression monitoring.
Microarray data often suffers from a low sample-to-gene ratio, impacting classification accuracy.
Gene selection is vital for improving predictive accuracy and identifying disease marker genes.

Purpose of the Study:

To develop a novel gene selection framework that overcomes limitations of existing methods.
To enhance the accuracy of identifying potential cancer marker genes.
To leverage Gene Ontology information to compensate for microarray data limitations.

Main Methods:

A hybrid approach combining Fisher method and Support Vector Machine-Recursive Feature Elimination (SVM-RFE).
Incorporation of a redundancy reduction stage to refine gene selection.
Integration of Gene Ontology data alongside gene expression values.

Main Results:

The proposed method demonstrated improved classification performance (accuracy, sensitivity, specificity) on colon, DLBCL, and prostate cancer datasets.
Selected genes' molecular functions support their involvement in cancer development.
The framework effectively identified potential cancer marker genes.

Conclusions:

The novel framework effectively addresses weaknesses in conventional gene selection methods.
Utilizing Gene Ontology and a redundancy reduction stage enhances marker gene prediction accuracy.
Identified marker genes for colon, DLBCL, and prostate cancer offer candidates for further research and potential therapeutic development.