Egon Willighagen

The [Self-organizing
map](https://en.wikipedia.org/wiki/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](http://www.dq.fct.unl.pt/staff/jas/) published a paper
that uses this method to analyze the whole [KEGG pathway
database](http://www.genome.jp/kegg/pathway.html): *Genome-Scale
Classification of Metabolic Reactions: A Chemoinformatics Approach*
(DOI: [anie.200503833](https://doi.org/10.1002/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](https://doi.org/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](https://en.wikipedia.org/wiki/EC_number) is
striking.

#### References

- [10.1002/ANIE.200503833](https://doi.org/10.1002/ANIE.200503833)
- [10.1021/CI0502707](https://doi.org/10.1021/CI0502707)


Genome‐Scale Classification of Metabolic Reactions: A Chemoinformatics Approach

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:
<em>
 Genome-Scale Classification of Metabolic Reactions: A Chemoinformatics Approach
</em>
(DOI: anie.200503833).  The method is based on earlier

Messaggi di Rogue Scholar

Mining the KEGG pathway database with self-organizing maps