  Recently, Antimicrobial peptides (AMPs) have been an area of interest in the
researches, as the first line of defense against the bacteria. They are raising
attention as an efficient way of fighting multidrug resistance. Discovering and
identification of AMPs in the wet labs are challenging, expensive, and
time-consuming. Therefore, using computational methods for AMP predictions have
grown attention as they are more efficient approaches. In this paper, we
developed a promising ensemble learning algorithm that integrates well-known
learning models to predict AMPs. First, we extracted the optimal features from
the physicochemical, evolutionary, and secondary structure properties of the
peptide sequences. Our ensemble algorithm then trains the data using
conventional algorithms. Finally, the proposed ensemble algorithm has improved
the performance of the prediction by about 10% comparing to the traditional
learning algorithms
