title: Comparison of two variations of neural network approach to the prediction of protein folding pattern.
abstract: We have designed, trained and tested two types of neural networks for the prediction of protein folding pattern from sequence. Here we describe the differences in the networks and compare their performance on a variety of proteins. Both network representations are generally successful in predicting protein fold and can also be used together to confirm a prediction.
authors.affiliation: Department of Chemistry, University of California at Berkeley 94720, USA.
authors.forename: I
authors.initials: I
authors.lastname: Dubchak
authors.forename: S R
authors.initials: SR
authors.lastname: Holbrook
authors.forename: S H
authors.initials: SH
authors.lastname: Kim
mesh_terms: D000596:Amino Acids / Q000737:chemistry; D016208:Databases, Factual; D008958:Models, Molecular; D016571:Neural Networks, Computer; D017510:Protein Folding; D011506:Proteins / Q000737:chemistry; D015203:Reproducibility of Results; D017421:Sequence Analysis / Q000379:methods*
chemical_list: D000596:Amino Acids; D011506:Proteins