title: Neural network prediction of polyproline type II secondary structures.
abstract: This study considers detection of polyproline type II secondary structures from protein sequences. This difficult problem was handled with multilayer perceptron neural networks, which were found to be useful for such bioinformatics studies. Polyproline II secondary structures have not previously been tried to be predicted from sequences.
authors.affiliation: Department of Computer Science, 33014 University of Tampere, Finland.
authors.forename: M
authors.initials: M
authors.lastname: Siermala
authors.forename: M
authors.initials: M
authors.lastname: Juhola
authors.forename: M
authors.initials: M
authors.lastname: Vihinen
mesh_terms: D006801:Humans; D016571:Neural Networks, Computer; D010455:Peptides / Q000737:chemistry*; D017433:Protein Structure, Secondary; D020539:Sequence Analysis, Protein
chemical_list: D010455:Peptides; C011083:polyproline