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This article is part of the supplement: The 2009 International Conference on Bioinformatics & Computational Biology (BioComp 2009)

Open Access Research

Classification of genomic islands using decision trees and their ensemble algorithms

Dongsheng Che1*, Cory Hockenbury1, Robert Marmelstein1 and Khaled Rasheed2

Author Affiliations

1 Department of Computer Science, East Stroudsburg University, East Stroudsburg, PA 18301, USA

2 Department of Computer Science, University of Georgia, Athens, GA 30602, USA

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BMC Genomics 2010, 11(Suppl 2):S1  doi:10.1186/1471-2164-11-S2-S1

Published: 2 November 2010

Abstract

Background

Genomic islands (GIs) are clusters of alien genes in some bacterial genomes, but not be seen in the genomes of other strains within the same genus. The detection of GIs is extremely important to the medical and environmental communities. Despite the discovery of the GI associated features, accurate detection of GIs is still far from satisfactory.

Results

In this paper, we combined multiple GI-associated features, and applied and compared various machine learning approaches to evaluate the classification accuracy of GIs datasets on three genera: Salmonella, Staphylococcus, Streptococcus, and their mixed dataset of all three genera. The experimental results have shown that, in general, the decision tree approach outperformed better than other machine learning methods according to five performance evaluation metrics. Using J48 decision trees as base classifiers, we further applied four ensemble algorithms, including adaBoost, bagging, multiboost and random forest, on the same datasets. We found that, overall, these ensemble classifiers could improve classification accuracy.

Conclusions

We conclude that decision trees based ensemble algorithms could accurately classify GIs and non-GIs, and recommend the use of these methods for the future GI data analysis. The software package for detecting GIs can be accessed at http://www.esu.edu/cpsc/che_lab/software/GIDetector/ webcite.