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.
1 Data and Knowledge Engineering Laboratory, School of Information Technology, King Mongkut's University of Technology Thonburi, Bangkok, Thailand.
Worrawat
W
Engchuan
2 Department of Chemical Engineering, Faculty of Engineering, King Mongkut's University of Technology Thonburi, Bangkok, Thailand.
Asawin
A
Meechai
3 Biostatistics and Informatics Laboratory, Genome Technology Research Unit, National Center for Genetic Engineering and Biotechnology.
Sissades
S
Tongsima
4 Biological Engineering Program, Faculty of Engineering, King Mongkut's University of Technology Thonburi, Bangkok, Thailand.
Narumol
N
Doungpan
1 Data and Knowledge Engineering Laboratory, School of Information Technology, King Mongkut's University of Technology Thonburi, Bangkok, Thailand.
Jonathan H
JH
Chan
D000704:Analysis of Variance; D014408:Biomarkers, Tumor / Q000235:genetics*; D001943:Breast Neoplasms / Q000175:diagnosis* / Q000235:genetics; D015179:Colorectal Neoplasms / Q000175:diagnosis* / Q000235:genetics; D057225:Data Mining; D030541:Databases, Genetic; D005260:Female; D020869:Gene Expression Profiling / Q000379:methods; D015972:Gene Expression Regulation, Neoplastic; D006801:Humans; D008175:Lung Neoplasms / Q000175:diagnosis* / Q000235:genetics; D020411:Oligonucleotide Array Sequence Analysis / Q000379:methods; D012984:Software
D014408:Biomarkers, Tumor
Microarray; breast cancer; classification; colorectal cancer; feature selection; gene expression analysis; gene-set; lung cancer