title: In silico prediction of post-translational modifications.
abstract: Methods for predicting protein post-translational modifications have been developed extensively. In this chapter, we review major post-translational modification prediction strategies, with a particular focus on statistical and machine learning approaches. We present the workflow of the methods and summarize the advantages and disadvantages of the methods.
authors.affiliation: Department of Systems and Computer Science, Howard University, Washington, DC, USA. chunmei@scs.howard.edu
authors.forename: Chunmei
authors.initials: C
authors.lastname: Liu
authors.forename: Hui
authors.initials: H
authors.lastname: Li
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D019295:Computational Biology; D003198:Computer Simulation; D016208:Databases, Factual; D020407:Internet; D013058:Mass Spectrometry; D008954:Models, Biological; D011499:Protein Processing, Post-Translational; D012984:Software