| Item type |
文献 / Documents(1) |
| 公開日 |
2021-11-05 |
| アクセス権 |
|
|
アクセス権 |
open access |
| 資源タイプ |
|
|
資源タイプ識別子 |
http://purl.org/coar/resource_type/c_1843 |
|
資源タイプ |
other |
| 出版社版DOI |
|
|
|
識別子タイプ |
DOI |
|
|
関連識別子 |
https://doi.org/10.1101/2021.03.16.435601 |
|
|
言語 |
ja |
|
|
関連名称 |
10.1101/2021.03.16.435601 |
| 出版タイプ |
|
|
出版タイプ |
AM |
|
出版タイプResource |
http://purl.org/coar/version/c_ab4af688f83e57aa |
| タイトル |
|
|
タイトル |
Cluster Analysis of SARS-CoV-2 Gene using Deep Learning Autoencoder : Gene Profiling for Mutations and Transitions |
|
言語 |
en |
| タイトル別表記 |
|
|
その他のタイトル |
SARS-CoV-2 genome clusters analyzed by Deep Learning |
|
言語 |
en |
| 著者 |
ミヤケ, ジュン
サトウ, タカアキ
ババ, シュンスケ
ナカムラ, ハヤオ
ニイオカ, ヒロヒコ
中澤, 慶久
|
| 抄録 |
|
|
内容記述タイプ |
Abstract |
|
内容記述 |
We report on a method for analyzing the variant of coronavirus genes using autoencoder. Since coronaviruses have mutated rapidly and generated a large number of genotypes, an appropriate method for understanding the entire population is required. The method using autoencoder meets this requirement and is suitable for understanding how and when the variants emarge and disappear. For the over 30,000 SARS-CoV-2 ORF1ab gene sequences sampled globally from December 2019 to February 2021, we were able to represent a summary of their characteristics in a 3D plot and show the expansion, decline, and transformation of the virus types over time and by region. Based on ORF1ab genes, the SARS-CoV-2 viruses were classified into five major types (A, B, C, D, and E in the order of appearance): the virus type that originated in China at the end of 2019 (type A) practically disappeared in June 2020; two virus types (types B and C) have emerged in the United States and Europe since February 2020, and type B has become a global phenomenon. Type C is only prevalent in the U.S. and is suspected to be associated with high mortality, but this type also disappeared at the end of June. Type D is only found in Australia. Currently, the epidemic is dominated by types B and E. |
|
言語 |
en |
| キーワード |
|
|
言語 |
en |
|
主題Scheme |
Other |
|
主題 |
Autoencoder |
| キーワード |
|
|
言語 |
en |
|
主題Scheme |
Other |
|
主題 |
Deep Learning |
| キーワード |
|
|
言語 |
en |
|
主題Scheme |
Other |
|
主題 |
SARS-CoV-2 |
| キーワード |
|
|
言語 |
en |
|
主題Scheme |
Other |
|
主題 |
Genome |
| キーワード |
|
|
言語 |
en |
|
主題Scheme |
Other |
|
主題 |
Mutation |
| キーワード |
|
|
言語 |
en |
|
主題Scheme |
Other |
|
主題 |
Classification |
| キーワード |
|
|
言語 |
en |
|
主題Scheme |
Other |
|
主題 |
Cluster |
| 書誌情報 |
発行日 2021-03-16
|
| 備考 |
|
|
言語 |
ja |
|
値 |
This article is a preprint and has not been certified by peer review. |
| 権利情報 |
|
|
言語 |
en |
|
権利情報 |
The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. All rights reserved. No reuse allowed without permission. |
| EID |
|
|
識別子 |
381976 |
|
識別子タイプ |
URI |
| 言語 |
|
|
言語 |
eng |
biorxiv_20210316.pdf (1.3 MB)
