In silico prediction of promoter sequences of Bacillus species

The understanding of the gene regulation process, even with the advances of the in vitro and in silico techniques, has been one of the main challenges for the molecular biologists. In this context, an important regulatory mechanisms are the promoters regions, which promote the initialization of the gene expression process. In this paper, we present an empirical comparison of machine learning techniques such as naive Bayes classifier, decision trees, support vector machines and neural networks to the task of promoter prediction. In order to do so, we first build a hybrid dataset of promoter and non-promoter sequences for six different species of Bacillus: subtilis, liqueniformis, cereus, megaterium, thurigiensis, and firmus.