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Updated: Jun 19, 2026
10:17
An Allele-specific Gene Expression Assay to Test the Functional Basis of Genetic Associations
Published on: November 3, 2010
Gene association analysis: a survey of frequent pattern mining from gene expression data
Ronnie Alves1, Domingo S Rodriguez-Baena, Jesus S Aguilar-Ruiz
1Institute of Developmental Biology and Cancer, Centre de Biochimie, Faculte des Sciences, the University of Nice, 06108 Nice cedex 2. alves@unice.fr
Briefings in Bioinformatics
|October 10, 2009
Summary
Frequent pattern mining (FPM) aids genomic studies by finding gene associations in DNA microarray data. Current methods balance scalability and biological relevance, with integrative approaches showing promise.
Area of Science:
- Genomics
- Bioinformatics
- Data Mining
Background:
- Genomic studies aim to associate variables, but DNA microarray data presents unique analysis challenges.
- Frequent Pattern Mining (FPM) is a data analysis strategy successfully applied in various fields.
Purpose of the Study:
- To review relevant Frequent Pattern Mining (FPM) strategies for gene association analysis (GAA) in microarray data.
- To identify key issues in developing efficient and practical GAA methods.
Main Methods:
- Review of existing Frequent Pattern Mining (FPM) strategies.
- Analysis of challenges in applying FPM to gene expression data.
Main Results:
- Scalability of FPM methods does not guarantee biological soundness of discovered gene associations, and vice versa.
- Existing methods often lack a balance between computational efficiency and biological relevance.
Conclusions:
- Gene association analysis (GAA) requires balanced mining models incorporating best practices.
- Integrative approaches that leverage biological knowledge during mining are increasingly reliable and recommended.
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