Self-organizing maps
Convergence and ordering of Kohonen's batch map
Neural Computation
KES '08 Proceedings of the 12th international conference on Knowledge-Based Intelligent Information and Engineering Systems, Part III
XML data clustering: An overview
ACM Computing Surveys (CSUR)
Soft topographic maps for clustering and classifying bacteria using housekeeping genes
Advances in Artificial Neural Systems
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New, more effective software tools are needed for the analysis and organization of the continually growing biological databases. An extension of the Self-Organizing Map (SOM) is used in this work for the clustering of all the 77,977 protein sequences of the SWISS-PROT database, release 37. In this method, unlike in some previous ones, the data sequences are not converted into histogram vectors in order to perform the clustering. Instead, a collection of true representative model sequences that approximate the contents of the database in a compact way is found automatically, based on the concept of the generalized median of symbol strings, after the user has defined any proper similarity measure for the sequences such as Smith-Waterman, BLAST, or FASTA. The FASTA method is used in this work. The benefits of the SOM and also those of its extension are fast computation, approximate representation of the large database by means of a much smaller, fixed number of model sequences, and an easy interpretation of the clustering by means of visualization. The complete sequence database is mapped onto a two-dimensional graphic SOM display, and clusters of similar sequences are then found and made visible by indicating the degree of similarity of the adjacent model sequences by shades of gray.