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Modeling the Effects of Toxins in Metabolic Networks

Alireza Tamaddoni Nezhad, Raphael Chaleil, Antonis Kakas, Michael Sternberg, J Nicholson, Stephen Muggleton

Journal Article
IEEE Engineering in Medicine and Biology
Volume 26
pp.37–46
2007
DOI 10.1109/MEMB.2007.335590
Abstract

Abduction and induction are two forms of reasoning that have been widely used in machine learning. The combination of abduction and induction has recently been explored from a number of angles, one of which is the area of systems biology. The research reported in this article is being conducted as part of the MetaLog project, which aims to build causal models of the actions of toxins from empirical data in the form of nuclear magnetic resonance (NMR) data, together with information on networks of known metabolic reactions from the Kyoto Encyclopedia of Genes and Genomes (KEGG). The NMR spectra provide information concerning the flux of metabolite concentrations before, during, and after administration of a toxin

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