Review of Imbalanced Data Learning for Protein Methylation Prediction

In this chapter, the authors focus on the study of computational predictions on this particular PTM-protein arginine methylation. They provide a comprehensive review of the study of arginine methylation prediction. They extensively investigate all existing methylation prediction methods and servers; thoroughly review all feature extracting schemes used for sequence encoding; and carefully summarize and compare all processing steps in their methodologies, including data collection, feature extraction and selection, classifier training and evaluation, and result discussion. Finally, the authors suggest several future directions that are worthy of continuous research on methylation predictions.