title: A new representation for protein secondary structure prediction based on frequent patterns.
abstract: BACKGROUND
  A new representation for protein secondary structure prediction based on frequent amino acid patterns is described and evaluated. We discuss in detail how to identify frequent patterns in a protein sequence database using a level-wise search technique, how to define a set of features from those patterns and how to use those features in the prediction of the secondary structure of a protein sequence using support vector machines (SVMs).
  
  
  RESULTS
  Three different sets of features based on frequent patterns are evaluated in a blind testing setup using 150 targets from the EVA contest and compared to predictions of PSI-PRED, PHD and PROFsec. Despite being trained on only 940 proteins, a simple SVM classifier based on this new representation yields results comparable to PSI-PRED and PROFsec. Finally, we show that the method contributes significant information to consensus predictions.
  
  
  BACKGROUND
  The method is available from the authors upon request.
authors.affiliation: Practical Informatics and Bioinformatics Group, Department of Informatics, Ludwig-Maximilians-University Amalienstrasse 17, D-80333 München, Germany.
authors.forename: Fabian
authors.initials: F
authors.lastname: Birzele
authors.forename: Stefan
authors.initials: S
authors.lastname: Kramer
mesh_terms: D000465:Algorithms; D000595:Amino Acid Sequence; D003198:Computer Simulation; D008956:Models, Chemical; D008958:Models, Molecular; D008969:Molecular Sequence Data; D017433:Protein Structure, Secondary; D011506:Proteins / Q000737:chemistry* / Q000648:ultrastructure; D016415:Sequence Alignment / Q000379:methods*; D020539:Sequence Analysis, Protein / Q000379:methods*
chemical_list: D011506:Proteins