title: Protein secondary structure prediction using two-level case-based reasoning.
abstract: We have developed a two-level case-based reasoning architecture for predicting protein secondary structure. The central idea is to break the problem into two levels: first, reasoning at the object (protein) level, and using the global information from this level to focus on a more restricted problem space; second, decomposing objects into pieces (segments), and reasoning at the internal structures level; finally, synthesizing the pieces back to the objects. The architecture has been implemented and tested on a commonly used data set with 69.3% predictive accuracy. It was then tested on a new data set with 67.3% accuracy. Additional experiments were conducted to determine the effects of using different similarity matrices.
authors.affiliation: Department of Computer Science, University of Pittsburgh, PA 15260, USA.
authors.forename: B
authors.initials: B
authors.lastname: Leng
authors.forename: B G
authors.initials: BG
authors.lastname: Buchanan
authors.forename: H B
authors.initials: HB
authors.lastname: Nicholas
mesh_terms: D000595:Amino Acid Sequence; D000818:Animals; D001185:Artificial Intelligence; D053818:Chymases; D016208:Databases, Factual; D005544:Forecasting; D008969:Molecular Sequence Data; D010191:Pancreatic Polypeptide / Q000737:chemistry; D017433:Protein Structure, Secondary; D051381:Rats; D015203:Reproducibility of Results; D017421:Sequence Analysis / Q000379:methods*; D012697:Serine Endopeptidases / Q000737:chemistry
chemical_list: D010191:Pancreatic Polypeptide; D012697:Serine Endopeptidases; D053818:Chymases