Finally, we discuss two deep learning methods that address the de novo sequencing task by post-processing the results of an existing de novo sequencing method. The pNovo 3 algorithm re-ranks a given set of de novo predictions by using a deep learning model. The method builds upon pNovo+ , which uses a spectrum graph-based algorithm for de novo sequencing. In pNovo 3, the top 10 predicted candidate peptides are retained and provided as input to the pDeep deep learning model, which predicts fragment ion intensities . A set of features, based on the pDeep output, are computed, and these feature vectors are used to train a ranking support vector machine (SVM) . The final output of the trained model is the top-scoring candidate peptide, as determined by the SVM.