About EGID

Genomic islands (GIs) are genomic segments that are originally transferred from viruses or other organisms. The detection of genomic islands in genomes can lead to many applications in indu- strial, medical and environmental contexts. Current computational tools developed for GI detection either suffer low recall or low pre- cision, and thus an accurate prediction tool is in great demand. In this paper, we report the development of our Ensemble algorithm for Genomic Island Detection (EGID). EGID utilizes the prediction results of existing computational tools, filters and generates consensus pre- diction results. Performance comparisons between our ensemble algorithm and existing programs have shown that our ensemble algo- rithm is better than any other program, strongly suggesting that consensus results are more reliable when using for GI detection.

Software availability

Disclaimer: this software is free to use, modify, redistribute without any restrictions.

Supplementary data


Dongsheng Che, Shabbir Hasan, Han Wang, John Fazekas, Jinling Huang, and Qi Liu - EGID: an ensemble algorithm for improved genomic island detection in genomic sequences, Bioinformation. 7(6): 311-314 (2011)

Dongsheng Che, Shabbir Hasan, Han Wang, John Fazekas, Bernard Chen and Xiuping Tao - M Are Better Than One: An Ensemble Method For Genomic Island Prediction. Proceedings of the 6th International Conference on Bioinformatics and Biomedical Engineering (iCBBE 2012).


Bugs, problems, suggestions can be reached to
Dongsheng Che: dche@po-box.esu.edu

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Page last updated: Feb. 16, 2012