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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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
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author Miyake, Jun
Kaneshita, Yuhei
Asatani, Satoshi
Tagawa, Seiichi
Niioka, Hirohiko
Hirano, Takashi
author_facet Miyake, Jun
Kaneshita, Yuhei
Asatani, Satoshi
Tagawa, Seiichi
Niioka, Hirohiko
Hirano, Takashi
author_sort Miyake, Jun
collection PubMed
description 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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spelling pubmed-58521912018-03-21 Graphical classification of DNA sequences of HLA alleles by deep learning Miyake, Jun Kaneshita, Yuhei Asatani, Satoshi Tagawa, Seiichi Niioka, Hirohiko Hirano, Takashi Hum Cell Rapid Communication 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. Springer Japan 2018-01-11 2018 /pmc/articles/PMC5852191/ /pubmed/29327117 http://dx.doi.org/10.1007/s13577-017-0194-6 Text en © The Author(s) 2018 Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.
spellingShingle Rapid Communication
Miyake, Jun
Kaneshita, Yuhei
Asatani, Satoshi
Tagawa, Seiichi
Niioka, Hirohiko
Hirano, Takashi
Graphical classification of DNA sequences of HLA alleles by deep learning
title Graphical classification of DNA sequences of HLA alleles by deep learning
title_full Graphical classification of DNA sequences of HLA alleles by deep learning
title_fullStr Graphical classification of DNA sequences of HLA alleles by deep learning
title_full_unstemmed Graphical classification of DNA sequences of HLA alleles by deep learning
title_short Graphical classification of DNA sequences of HLA alleles by deep learning
title_sort graphical classification of dna sequences of hla alleles by deep learning
topic Rapid Communication
url 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
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