Classification of genomic islands using decision trees and their ensemble algorithms

Dongsheng Che, Cory Hockenbury, Robert Marmelstein, Khaled Rasheed
2010 BMC Genomics  
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
more » ... ification 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
doi:10.1186/1471-2164-11-s2-s1 pmid:21047376 pmcid:PMC2975412 fatcat:ppwcuhl4fzgurdvtunaue6qyzq