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Bioinformatics Vol. 15 no. 12 1999
Pages 1039-1046
© 1999 Oxford University Press

Decision tree-based formation of consensus protein secondary structure prediction

Joachim Selbig 1, Theo Mevissen 1 and Thomas Lengauer 1

1 Institute for Algorithms and Scientific Computing (SCAI), GMD—German National Research Center for Information Technology, SchloßBirlinghoven, D-53754 Sankt Augustin, Germany

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.

Availability: Our method for consensus secondary structure prediction CoDe (Consensus formation by Decision tree learning) is based on machine learning and will be integrated into the ToPLign system (Toolbox for Protein aLignment) which can be accessed at http://cartan.gmd.de/ToPLign.html.

Contact: {mevissen,selbig}{at}gmd.de

Received on March 30, 1999 ; revised on June 17, 1999 ; accepted on June 21, 1999

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