Machine Learning Techniques for Predicting Bacillus subtilis Promoters

One of the most important goals of bioinformatics is the ability to identify genes in uncharacterized DNA sequences. Improved promoter prediction methods can be one step towards developing more reliable ab initio gene prediction methods. In this paper, we present an empirical comparison of machine learning techniques such as Naive Bayes, Decision Trees, Support Vector Machines and Neural Networks to the task of predicting Bacillus subtilis promoters. In order to do so, we first built a data set of promoter and nonpromoter sequences for this organism.