TY  - JOUR
A1  - Flöter, André
A1  - Nicolas, Jacques
A1  - Schaub, Torsten H.
A1  - Selbig, Joachim
T1  - Threshold extraction in metabolite concentration data
N2  - 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
Y1  - 2004
ER  - 
TY  - JOUR
A1  - Flöter, André
A1  - Nicolas, Jacques
A1  - Schaub, Torsten H.
A1  - Selbig, Joachim
T1  - Threshold extraction in metabolite concentration data
Y1  - 2003
UR  - http://www.cs.uni-potsdam.de/wv/pdfformat/floeterGCB2003.pdf
ER  - 
TY  - JOUR
A1  - Flöter, André
A1  - Selbig, Joachim
A1  - Schaub, Torsten H.
T1  - Finding metabolic pathways in decision forests
Y1  - 2004
SN  - 3-540-23221-4
ER  -