Che et al. BMC Genomics 2010, 11(Suppl 2):S1 http://www.biomedcentral.com/1471-2164/11/S2/S1 Katz [7] discovered that one PAI in the chromosome of Dichelobacter nodosus carries an integrase, which was acquired from Escherichia coli phage. GIs are usually flanked by direct repeat (DR) sequences, in which each DR is 16-20 long with nearly perfect sequence repeti- tion, or inverted repeat sequence elements (IS) [8]. In addition, the mobile gene products usually play the roles in inserting and excising of the genomic regions by recombination between the flanking repeats [9]. Another interesting property Hacker and Kaper found is that 75% of the insertion sites of GIs are at the 3′-end of a trans- fer RNAs (tRNAs) [2]. Another interesting feature that can tells GIs from non-GIs is based on the sequence composition of the genome. Typically, each genome generally has its own unique sequence composition signature, and thus the sequence compositions between GIs, which are from an alien genome, and the rest of the host genome are different. For instance, the measurement of guanine and cytosine (G+C) contents in a chromosome showed that 20-30% genomic regions carried atypical G+C contents which were possibly GI-associated [10]. The combination of codon bias and Codon Adaption Index (CAI) was used to detect alien genomic regions [11,12]. Besides, Karlin [13] used dinucleotide fre- quency difference (δ* difference) to identify possible GIs. In order to improve the discrimination power for detecting alien gene clusters, Tsirigos and Rigoutsos [14] extended the 2 mers (i.e., δ* difference) to 8-9 mers. Recently, Vernikos and Parkhill [18] proposed a new model, interpolated variable order motifs, to detect horizontally acquired genes. This new method overcomes the low discrimination power problem using the lower-order motif models, and the extremely low frequency problem of observed motifs using the higher-order motif models. In order to improve the detection power, the integra- tion of multiple GI-associated features for detecting GIs may be applied. IslandPath [16] is a web-server that dis- plays the G+C contents of open reading frames (ORFs), δ* difference (dinucleotide), the location of mobile genes, and the location of tRNAs. IslandPath leaves users to judge whether a genomic region are GIs or not, based on provided multiple feature values. A recent study on IslandPath has shown that using the feature of δ* difference only leads to the low specificity problem, while using the combined features of δ* difference and mobile gene leads to the low sensitivity problem [6]. Garcia-Vallve et al. [17] used a simple rule-based algo- rithm to identify horizontally acquired gene cluster. The gene cluster is considered to be horizontally acquired if either the G+C content and codon usage deviate by more than 1.5 standard deviations from the mean values, or the G+C content is extremely high or low. Page 2 of 9 Recently, Vernikos and Parkhill [18] combined multiple GI-associated features such as sequence composition and mobile gene, and used Relevance Vector Machine (RVM), a model similar to Support Vector Machines (SVMs) but exploiting fewer basis functions, to classify GIs. While multiple features have been used in a few studies previously, comprehensive machine learning approaches and performance comparison have not been systematically studied, leaving room for improvement for predicting GIs. In this paper, we present our work about classifying several genomic island datasets using supervised machine learning algorithms, and show that decision tree method perform better than other machine learning models including naive Bayesian, Bayesian networks, neural networks, simple logistic and support vector machines (SVMs) in general. We will show decision tree based ensemble algorithms can further improve classifi- cation accuracy by up to 5.9%. Results and discussion Feature analysis In this study, we used the datasets of GIs and non-GIs from three genera: Salmonella, Staphylococcus, and Streptococcus. For each instance (either GI or non-GI) of these datasets, eight feature values, i.e., Interpolated Variable Order Motif (IVOM), Insert point, Size, Den- sity, Repeats, Integrase, Phage and RNA, were obtained. The description summary of the eight features is listed in Table 1 (See Methods for more details). In order to evaluate each of eight features, we define the signal to noise ratio (G2N) as the distance of the arithmetic means of the GI and non-GI classes divided by the sum of the corresponding standard deviations, i.e., G2N = |  GI −  non_GI |  GI +  non_GI (1) Table 1 The descriptions of the features associated with genomic islands Feature IVOM Insertion Point (IP) Size Density Repeats Integrase Phage RNA Description Interpolated Variable Order Motif compositional score (Relative Entropy) Binary: “1” if within a CDS locus, “0” otherwise Size of each genomic region in bp Number of genes per kb Binary: “1” if repeats present, “0” otherwise Binary: “1” if containing integrase-like protein domain, “0” otherwise Binary: “1” if containing phage-related protein domain, “0” otherwise Binary: “1” if containing non-coding RNA, “0” otherwise