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Bioinformatics 20(10) © Oxford University Press 2004; all rights reserved.

GCB Conference Paper

Threshold extraction in metabolite concentration data

A. Flöter 1,*, J. Nicolas 3, T. Schaub 1 and J. Selbig 2

1 University of Potsdam, Institute for Computer Science, August-Bebel-Str. 89/Hs. 4, 14482 Potsdam, Germany, 2 Max-Planck-Institute of Molecular Plant Physiology, 14424 Potsdam, Germany and 3 Institut de Recherche en Informatique et Systèmes Aléatoires, Campus Universitaire de Beaulieu, 35042 Rennes cedex, France

Received on October 12, 2003; accepted on February 3, 2004

Motivation: Continued development of analytical techniques based on gas chromatography and mass spectrometry now facilitates the generation of larger sets of metabolite concentration data. An important step towards the understanding of metabolite dynamics is the recognition of stable states where metabolite concentrations exhibit a simple behaviour. Such states can be characterized through the identification of significant thresholds in the concentrations. But general techniques for finding discretization thresholds in continuous data prove to be practically insufficient for detecting states due to the weak conditional dependences in concentration data.

Results: We introduce a method of recognizing states in the framework of decision tree induction. It is based upon a global analysis of decision forests where stability and quality are evaluated. It leads to the detection of thresholds that are both comprehensible and robust. Applied to metabolite concentration data, this method has led to the discovery of hidden states in the corresponding variables. Some of these reflect known properties of the biological experiments, and others point to putative new states.

Availability: An implementation of this approach can be obtained from the authors upon request.

Contact: floeter{at}cs.uni-potsdam.de

* To whom correspondence should be addressed.


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