Predicting protein localization using a domain adaptation na¨ıve Bayes classifier with burrows wheeler transform features

The reduced cost of the next generation sequencing technologies provides opportunities to study non-model organisms. However, one challenge is the large volume of data generated and, thus, the need to use automated approaches to annotate these data. Machine learning algorithms could provide a cost-effective solution but they need lots of labeled data and informative features to represent these data. Our proposed approach addresses both these problems by using a domain adaptation classifier in conjunction with features generated with unsupervised techniques to annotate biological sequence data.