基于深度学习的 HLA 等位基因 DNA 序列的图形分类。
Graphical classification of DNA sequences of HLA alleles by deep learning.
机构信息
Graduate School of Engineering Science, Osaka University, 1-3 Machikane-Yama, Toyonaka, Osaka, 560-8531, Japan.
Global Center for Medical Engineering and Informatics, Osaka University, Building A-301, 1-3 Yamadaoka, Suita, Osaka, 565-0871, Japan.
出版信息
Hum Cell. 2018 Apr;31(2):102-105. doi: 10.1007/s13577-017-0194-6. Epub 2018 Jan 11.
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.
人类白细胞抗原 (HLA)-A DNA 的等位基因通过使用人工智能“深度学习（堆叠自动编码器）”进行分类和图形表达。从免疫多态性数据库中收集的长度为 822 bp 的核苷酸序列数据被压缩到 2 维表示并进行绘制。二维图谱的图谱表明，可以将等位基因分类为形成的聚类。HLA-A DNA 的二维图谱为表征各种等位基因提供了清晰的前景。
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