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Finding Metabolic Pathways in Decision Forests

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Part of the Studies in Classification, Data Analysis, and Knowledge Organization book series (STUDIES CLASS)

Abstract

Data of metabolite concentrations in tissue samples is a powerful source of information about metabolic activity in organisms. Several methods have already been reported that allow for inferences about genetic regulatory networks using expression profiling data. Here we adapted these techniques for metabolic concentration data. While somewhat accurate in predicting certain properties these methods encounter problems when dealing with networks containing a high number of vertices (τ 50). To circumvent these difficulties, larger data sets are usually reduced a priori by means of preprocessing techniques. Our approach allows to make network inferences using ensembles of decision trees that can handle almost any amount of vertices, and thus to avoid time consuming preprocessing steps. The technique works on a bootstrap principle and heuristically searches for partially correlated relations between all objects. We applied this approach to synthetically generated data as well as on data taken from real experiments.

Keywords

  • Decision Tree
  • Partial Correlation
  • Target Variable
  • Target Attribute
  • Genetic Regulatory Network

These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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© 2005 Springer-Verlag Berlin · Heidelberg

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Flöter, A., Selbig, J., Schaub, T. (2005). Finding Metabolic Pathways in Decision Forests. In: Baier, D., Wernecke, KD. (eds) Innovations in Classification, Data Science, and Information Systems. Studies in Classification, Data Analysis, and Knowledge Organization. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-26981-9_24

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