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In this paper we present an approach based on symbolic regression (using genetic programming) that helps to distinguish between target and decoy matches. On the basis of features calculated for matched sequences and using the information on the original sequence set (target or decoy) we learn mathematical models that calculate updated scores. As an alternative to this white box modelling approach we also use a black box modelling method, namely random forests.
As we show in the empirical section of this paper, this approach leads to scores that increase the number of reliably identified samples that are originally scored using the MS Amanda identification algorithm for high resolution as well as for low resolution mass spectra.",
Genetic Programming entries for Viktoria Dorfer Sergey Maltsev Stephan Dreiseitl Karl Mechtler Stephan M Winkler