Gene-set activity toolbox (GAT): A platform for microarray-based cancer diagnosis using an integrative gene-set analysis approach
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Author list: Engchuan W., Meechai A., Tongsima S., Doungpan N., Chan J.H.
Publisher: World Scientific Publishing
Publication year: 2016
Journal: Journal of Bioinformatics and Computational Biology (0219-7200)
Volume number: 14
Issue number: 4
ISSN: 0219-7200
eISSN: 1757-6334
Languages: English-Great Britain (EN-GB)
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Abstract
Cancer is a complex disease that cannot be diagnosed reliably using only single gene expression analysis. Using gene-set analysis on high throughput gene expression profiling controlled by various environmental factors is a commonly adopted technique used by the cancer research community. This work develops a comprehensive gene expression analysis tool (gene-set activity toolbox: (GAT)) 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. GAT can be accessed from http://gat.sit.kmutt.ac.th where GAT's Java library for gene-set analysis, simple classification and a database with three cancer benchmark datasets can be downloaded. ฉ 2016 The Author(s).
Keywords
colorectal cancer, gene-set
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