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A genomic island (GI) is a genomic segment in a host genome, and it was transferred from donor genomes. Since genomic islands (GIs) usually contain important genes such as pathogenicity genes, the detection of GIs becomes extremely critical to medical research and industrial applications. Previous computational GI detection tools used one or a few GI-associate features, and thus they suffered the problem of low prediction accuracy. A systematic approach that uses multiple sources to improve GI prediction accuracy, therefore, is in great demand. In this paper, we report the development of Genomic Island Hunter (GIHunter), an accurate software tool for GI detection. GIHunter is a decision tree based bagging model that uses eight GI-associated features such as sequence composition, mobile gene information, and integrase. The performance metric comparison between our approach and other current existing GI prediction methods has shown that our approach is more accurate than other approaches. We have used GIHunter to predict GIs in more than 2000 prokaryotic genomes. We have also visualized our predicted GIs so that our predicted results become useful and meaningful for biomedical studies. Our GI program GIHunter can be obtained at: http://www.esu.edu/cpsc/che_lab/software/GIHunter. Our GI prediction results are available on our genomic islands database, which is: http://www5.esu.edu/cpsc/bioinfo/dgi.
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