title: Decision tree-based formation of consensus protein secondary structure prediction.
abstract: BACKGROUND
  Prediction of protein secondary structure provides information that is useful for other prediction methods like fold recognition and ab initio 3D prediction. A consensus prediction constructed from the output of several methods should yield more reliable results than each of the individual methods.
  
  
  METHODS
  We present an approach that reveals subtle but systematic differences in the output of different secondary structure prediction methods allowing the derivation of coherent consensus predictions. The method uses a machine learning technique that builds decision trees from existing data.
  
  
  RESULTS
  The first results of our analysis show that consensus prediction of protein secondary structure may be improved both quantitatively and qualitatively.
authors.affiliation: Institute for Algorithms and Scientific Computing (SCAI), GMD-German National Research Center for Information Technology, Sankt Augustin, Germany.
authors.forename: J
authors.initials: J
authors.lastname: Selbig
authors.forename: T
authors.initials: T
authors.lastname: Mevissen
authors.forename: T
authors.initials: T
authors.lastname: Lengauer
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D003663:Decision Trees; D017433:Protein Structure, Secondary / Q000235:genetics*; D015203:Reproducibility of Results; D018511:Systems Integration