Gene-set Activity Toolbox or GAT
This is a web-based application, which provides several computational tools and materials for supporting the gene expression study including,
the simple analysis tool to identify genetic markers, the gene-set activity transformation to analyze the data more systematically,
the machine learning tools from WEKA as well as we also provide the 15 benchmark gene-set activity datasets.
Fig.1 shows the workflow of GAT. Initially, this tool allows to analyze gene expression data from Gene Expression Omnibus (GEO) only. After retriving the dataset from GEO, the process will be started from pre-process, gene-set activity transformation, the classification to the biological intepretation of results.
Citing us
- Engchuan, W., Chan, J. H.: Pathway activity transformation for multi-class classification
of lung cancer datasets. Neurocomputing, in press (2014), doi:10.1016/j.neucom.2014.08.096
- Sootanan, P., Prom-on, S., Meechai, A., & Chan, J. H.: Pathway-based microarray analysis for robust disease classification.
Neural Computing and Applications, 21(4), 649-660 (2012)
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Fig.1 Overview of GAT.
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