In Silico Identification of Anticancer Peptides with Stacking Heterogeneous Ensemble Learning Model and Sequence Information

Cancer is a well-known dreadful killer of human being’s health, which has led to countless deaths and misery. Traditional treatment can also affect the normal cells while killing cancer cells. Meanwhile, physical or chemical techniques are costly and inefficient. Fortunately, anticancer peptides are a promising treatment, with specifically targeted, low production cost and other advantages. In order to effectively identify the anticancer peptides, we proposed a stacking heterogeneous ensemble learning model, ACP-SE, for predicting anticancer peptides. More specifically, to fully exploit protein sequence information, we developed an efficient feature representation approach by integrating binary profile feature and conjoint triad feature. Then we use a stacking ensemble strategy to combine the three heterogeneous classifiers and get the final prediction results. It was demonstrated that the proposed ACP-SE remarkably outperformed other comparison methods.