Protein secondary structure prediction using two-level case-based reasoning.
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.
Leng
B
B
Department of Computer Science, University of Pittsburgh, PA 15260, USA.
Buchanan
B G
BG
Nicholas
H B
HB
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
D010191:Pancreatic Polypeptide; D012697:Serine Endopeptidases; D053818:Chymases