Kohonen map as a visualization tool for the analysis of protein sequences: multiple alignments, domains and segments of secondary structures
Author(s) -
Jens Hanke,
J. Reich
Publication year - 1996
Publication title -
bioinformatics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 3.599
H-Index - 390
eISSN - 1367-4811
pISSN - 1367-4803
DOI - 10.1093/bioinformatics/12.6.447
Subject(s) - self organizing map , visualization , computer science , pattern recognition (psychology) , similarity (geometry) , artificial intelligence , artificial neural network , sequence (biology) , data mining , image (mathematics) , biology , genetics
The method of Kohonen maps, a special form of neural networks, was applied as a visualization tool for the analysis of protein sequence similarity. The procedure converts sequence (domains, aligned sequences, segments of secondary structure) into a characteristic signal matrix. This conversion depends on the property or replacement score vector selected by the user. Similar sequences have small distance in the signal space. The trained Kohonen network is functionally equivalent to an unsupervised non-linear cluster analyzer. Protein families, or aligned sequences, or segments of similar secondary structure, aggregate as clusters, and their proximity may be inspected on a color screen or on paper. Pull-down menus permit access to background information in the established text-oriented way.
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