Development of Neural Network for BLSOM Clustering of HA Genes of Avian Influenza Viruses Isolated in Guangdong Province

A neural network classification method, and a batch-learning self-organizing map (BLSOM), was established using trinucleotide and tetranucleotide in the hemagglutinin gene sequences of 25 avian influenza viruses isolated in Guangdong Province. Statistical analysis and normalization of the fragment number were done and MATLAB function was used to simulate the human brain thinking for self-organizing learning. When the number of training steps was 100 and above, the strains could be successfully clustered. H1, H3, H5, H7 and H9 subtype strains fell within different classes, respectively, and the HA gene cluster map of H3N2 and H7N9 strains was quite similar, suggesting that these strains shared the same origin; H5N1 strain was quite different in different years; H1N1 and H9N2 strains could be clustered into one group, indicating the natural recombinant variation in the two kinds of viruses, thereby providing a reference for high-risk strain screening and traceability.


Subject(s):
Issue Date:
2016-11
Publication Type:
Journal Article
PURL Identifier:
http://purl.umn.edu/253348
Published in:
Asian Agricultural Research, Volume 08, Issue 11
Page range:
101-104
Total Pages:
4




 Record created 2017-04-01, last modified 2017-08-29

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