A comprehensive gene expression analysis tool that is implemented with data retriever, traditional data pre-processing, several gene-set analysis methods, network visualization and data mining tools. The gene-set analysis methods are used to identify subsets of phenotype-relevant genes that will be used to build a classification model. To evaluate GAT performance, we performed a cross-dataset validation study on three common cancers namely colorectal, breast and lung cancers. The results show that GAT can be used to build a reasonable disease diagnostic model and the predicted markers have biological relevance.
Data and Knowledge Engineering Laboratory, School of Information Technology, King Mongkut's University of Technology Thonburi, Bangkok, Thailand; Department of Chemical Engineering, Faculty of Engineering, King Mongkut's University of Technology Thonburi, Bangkok, Thailand; Biostatistics and Informatics Laboratory, Genome Technology Research Unit, National Center for Genetic Engineering and Biotechnology; Biological Engineering Program, Faculty of Engineering, King Mongkut's University of Technology Thonburi, Bangkok, Thailand
GAT funding source(s)
This work was supported by a 2014 Office of Higher Education Grant (Wor-1) that is administered by King Mongkut's University of Technology Thonburi, the Higher Education Research Promotion and National Research University Project, Thailand's Office of the Higher Education Commission and by the Thailand Research Fund, grant number RSA58-80061.