Graphical classification of DNA sequences of HLA alleles by deep learning

Alleles of human leukocyte antigen (HLA)-A DNAs are classified and expressed graphically by using artificial intelligence “Deep Learning (Stacked autoencoder)”. Nucleotide sequence data corresponding to the length of 822 bp, collected from the Immuno Polymorphism Database, were compressed to 2-dimen...

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Detalles Bibliográficos
Autores principales: Miyake, Jun, Kaneshita, Yuhei, Asatani, Satoshi, Tagawa, Seiichi, Niioka, Hirohiko, Hirano, Takashi
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Springer Japan 2018
Materias:
Rapid Communication
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5852191/
https://www.ncbi.nlm.nih.gov/pubmed/29327117
http://dx.doi.org/10.1007/s13577-017-0194-6
Descripción
Sumario:Alleles of human leukocyte antigen (HLA)-A DNAs are classified and expressed graphically by using artificial intelligence “Deep Learning (Stacked autoencoder)”. Nucleotide sequence data corresponding to the length of 822 bp, collected from the Immuno Polymorphism Database, were compressed to 2-dimensional representation and were plotted. Profiles of the two-dimensional plots indicate that the alleles can be classified as clusters are formed. The two-dimensional plot of HLA-A DNAs gives a clear outlook for characterizing the various alleles. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (10.1007/s13577-017-0194-6) contains supplementary material, which is available to authorized users.

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