Decision free-based formation of consensus protein secondary structure prediction

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.