Mining the KEGG pathway database with self-organizing maps

The Self-organizing map (SOM) is a popular (again) and intuitive non-linear mapping method: it transforms a multidimensional space into two dimensions (normally: they are so easy to visualize). Latino and Aires-de-Sousa published a paper that uses this method to analyze the whole KEGG pathway database: Genome-Scale Classification of Metabolic Reactions: A Chemoinformatics Approach (DOI: anie.200503833).

The method is based on earlier work by Zhang and Aires-de-Sousa: Structure-Based Classification of Chemical Reactions without Assignment of Reaction Centers (DOI: 10.1021/ci0502707). A non-trivial feature of the suggested method is the use of two SOMs. The first maps the reaction onto a fixed-length vector (coined MOLMAP), which is used as input vector for the second map. This later map is used to cluster the KEGG reactions on a purely chemical basis. The resemblence with the EC numbering system is striking.