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Bioinformatics Vol. 19 no. 10 2003
Pages 1201-1207
© 2003 Oxford University Press

Toxicology analysis by means of the JSM-method

V. G. Blinova 1, D. A. Dobrynin 1, V. K. Finn 1, S. O. Kuznetsov 1,2,* and E. S. Pankratova 1

1 All-Russia Institute for Scientific and Technical Information (VINITI), Usievicha 20, 125219 Moscow, Russia
2 Institut für Algebra, Technische Universität Dresden, Mommsenstr. 13, 01062 Dresden, Germany

Received on December 25, 2001 ; revised on November 4, 2002 ; accepted on November 6, 2002

Motivation: A model for learning potential causes of toxicity from positive and negative examples and predicting toxicity for the dataset used in the Predictive Toxicology Challenge (PTC) is presented. The learning model assumes that the causes of toxicity can be given as substructures common to positive examples that are not substructures of negative examples. This assumption results in the choice of a learning model, called the JSM-method, and a language for representing chemical compounds, called the Fragmentary Code of Substructure Superposition (FCSS). By means of the latter, chemical compounds are represented as sets of substructures which are ‘biologically meaningful’ from the expert point of view.

Results: The chosen learning model and representation language show comparatively good performance for the PTC dataset: for three sex/species groups the predictions were ROC optimal, for one group the prediction was nearly optimal. The predictions tend to be conservative (few predictions and almost no errors), which can be explained by the specific features of the learning model.

Availability: by request to finn{at}viniti.ru; serge{at}viniti.ru, http://ki-www2.intellektik.informatik.tu-darmstadt.de/~jsm/QDA

Contact: serge{at}viniti.ru

* To whom correspondence should be addressed.


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