Fig. 1From: MultiToxPred 1.0: a novel comprehensive tool for predicting 27 classes of protein toxins using an ensemble machine learning approachFrom the total dataset of amino acid sequences corresponding to different types of protein toxins with different modes of action in the cell (n = 27) and non-toxins (n = 1) randomly generated, the molecular descriptors PAAC and DPC were calculated. Subsequently, eight machine learning algorithms were evaluated, first on a training dataset (80%) which was subjected to tenfold cross-validation. Then, the generated models were evaluated on a test dataset (20%) (independent dataset). The final stage consisted of selecting the best predictive model for its incorporation into a web application called MultiToxPred 1.0Back to article page