J Selbig1, T Mevissen, T Lengauer. 1. Institute for Algorithms and Scientific Computing (SCAI), GMD-German National Research Center for Information Technology, Sankt Augustin, Germany.
Abstract
MOTIVATION: 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. METHOD: 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.
MOTIVATION: 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. METHOD: 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: Niko Beerenwinkel; Barbara Schmidt; Hauke Walter; Rolf Kaiser; Thomas Lengauer; Daniel Hoffmann; Klaus Korn; Joachim Selbig Journal: Proc Natl Acad Sci U S A Date: 2002-06-11 Impact factor: 11.205