Gene-set activity toolbox (GAT): A platform for microarray-based cancer diagnosis using an integrative gene-set analysis approach.
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.
['Engchuan|Worrawat|W|', 'Meechai|Asawin|A|', 'Tongsima|Sissades|S|', 'Doungpan|Narumol|N|', 'Chan|Jonathan H|JH|']
D000704:Analysis of Variance D014408:Biomarkers, Tumor D001943:Breast Neoplasms D015179:Colorectal Neoplasms D057225:Data Mining D030541:Databases, Genetic D005260:Female D020869:Gene Expression Profiling D015972:Gene Expression Regulation, Neoplastic D006801:Humans D008175:Lung Neoplasms D020411:Oligonucleotide Array Sequence Analysis D012984:Software
gene-set gene expression analysis classification lung cancer Microarray breast cancer colorectal cancer feature selection