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Gene-set activity toolbox (GAT): A platform for microarray-based cancer diagnosis using an integrative gene-set
Worrawat Engchuan1, Asawin Meechai2, Sissades Tongsima3
11 Data and Knowledge Engineering Laboratory, School of Information Technology, King Mongkut's University of Technology Thonburi, Bangkok, Thailand.
Journal of Bioinformatics and Computational Biology
|April 23, 2016
Summary
This study introduces the gene-set activity toolbox (GAT), a new tool for analyzing gene expression data to improve cancer diagnosis. GAT aids in identifying relevant gene subsets for building accurate disease classification models.
Area of Science:
- Genomics
- Bioinformatics
- Cancer Research
Background:
- Single gene expression analysis is insufficient for reliable cancer diagnosis.
- Gene-set analysis of high-throughput gene expression profiling is a common technique in cancer research.
- Environmental factors significantly influence gene expression in cancer.
Purpose of the Study:
- To develop a comprehensive gene expression analysis tool (GAT).
- To integrate data retrieval, preprocessing, gene-set analysis, network visualization, and data mining.
- To identify phenotype-relevant gene subsets for building robust cancer classification models.
Main Methods:
- Development of the gene-set activity toolbox (GAT).
- Implementation of multiple gene-set analysis methods within GAT.
- Cross-dataset validation on colorectal, breast, and lung cancer datasets.
Main Results:
- GAT successfully builds reasonable disease diagnostic models.
- Identified predictive markers demonstrate biological relevance.
- Cross-dataset validation confirms the tool's efficacy across different cancer types.
Conclusions:
- GAT provides a comprehensive platform for gene expression analysis in cancer research.
- The tool facilitates the development of diagnostic models with biologically relevant markers.
- GAT is accessible online with downloadable resources for gene-set analysis and classification.
