title: A survey of machine learning methods for secondary and supersecondary protein structure prediction.
abstract: In this chapter we provide a survey of protein secondary and supersecondary structure prediction using methods from machine learning. Our focus is on machine learning methods applicable to β-hairpin and β-sheet prediction, but we also discuss methods for more general supersecondary structure prediction. We provide background on the secondary and supersecondary structures that we discuss, the features used to describe them, and the basic theory behind the machine learning methods used. We survey the machine learning methods available for secondary and supersecondary structure prediction and compare them where possible.
authors.affiliation: Department of Computer Science and Software Engineering, University of Melbourne, National ICT Australia, Parkville, VIC, Australia.
authors.forename: Hui Kian
authors.initials: HK
authors.lastname: Ho
authors.forename: Lei
authors.initials: L
authors.lastname: Zhang
authors.forename: Kotagiri
authors.initials: K
authors.lastname: Ramamohanarao
authors.forename: Shawn
authors.initials: S
authors.lastname: Martin
mesh_terms: D020816:Amino Acid Motifs; D001185:Artificial Intelligence; D019295:Computational Biology / Q000379:methods*; D008958:Models, Molecular; D017433:Protein Structure, Secondary; D011506:Proteins / Q000737:chemistry*
chemical_list: D011506:Proteins