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An approach is presented for computing meaningful pathways in the network of small molecule metabolism comprising the chemical reactions characterized in all organisms. The metabolic network is described as a weighted graph in which all the compounds are included, but each compound is assigned a weight equal to the number of reactions in which it(More)
Our knowledge of metabolism can be represented as a network comprising several thousands of nodes (compounds and reactions). Several groups applied graph theory to analyse the topological properties of this network and to infer metabolic pathways by path finding. This is, however, not straightforward, with a major problem caused by traversing irrelevant(More)
The analysis of a variety of data sets (transcriptome arrays, phylogenetic profiles, etc.) yields groups of functionally related genes. In order to determine their biological function, associated gene groups are often projected onto known pathways or tested for enrichment of known functions. However, these approaches are not flexible enough to deal with(More)
The aMAZE database (http://www.amaze.ulb.ac.be) manages information on the molecular functions of genes and proteins, their interactions and the biochemical processes in which they participate. Its data model embodies general rules for associating molecules and interactions into large complex networks that can be analysed using graph theory methods. The(More)
(2009). In response to "Can sugars be produced from fatty acids? A test case for pathway analysis tools". Retrieve-ensembl-seq: user-friendly and large-scale retrieval of single or multi-genome sequences from Ensembl. (2008). Machine learning techniques to identify putative genes involved in nitrogen catabolite repression in the yeast Saccharomyces(More)
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