Novel Feature Representation and Machine Learning Methods in Computational Proteomics

Abstract

In recent years, protein-related data have grown rapidly with the application of novel methods and techniques. Several online public databases have been set up, and investigators can easily retrieve various data reported in them. Traditional computational methods to deal with these data are becoming more and more inappropriate because they are in different forms. Thus, novel data-driven computational methods are increasingly needed. This thematic issue collects six excellent papers, out of which three papers reviewed newly proposed methods of essential problems in computational proteomics and three research articles proposed novel computational methods to deal with specific problems.