Time-Series Trend of Pandemic SARS-CoV-2 Variants Visualized Using Batch-Learning Self-Organizing Map for Oligonucleotide Compositions Cover

Time-Series Trend of Pandemic SARS-CoV-2 Variants Visualized Using Batch-Learning Self-Organizing Map for Oligonucleotide Compositions

Data Science Journal

Volume 20 (2021): Issue 1

By: Takashi Abe , Ryuki Furukawa, Yuki Iwasaki and Toshimichi Ikemura

Open Access

|Sep 2021

Authors

Takashi Abe

takaabe@ie.niigata-u.ac.jp

Smart Information Systems, Faculty of Engineering, Niigata University, Niigata-ken 950-2181

Ryuki Furukawa

t17b318c@mail.cc.niigata-u.ac.jp

Smart Information Systems, Faculty of Engineering, Niigata University, Niigata-ken 950-2181

Yuki Iwasaki

y_iwasaki@nagahama-i-bio.ac.jp

Department of Bioscience, Nagahama Institute of Bio-Science and Technology. Shiga-ken 526-0829

Toshimichi Ikemura

t_ikemura@nagahama-i-bio.ac.jp

Department of Bioscience, Nagahama Institute of Bio-Science and Technology. Shiga-ken 526-0829

DOI: https://doi.org/10.5334/dsj-2021-029 | Journal eISSN: 1683-1470

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Language: English

Submitted on: Mar 23, 2021

|

Accepted on: Sep 10, 2021

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Published on: Sep 21, 2021

Published by: Ubiquity Press

In partnership with: Paradigm Publishing Services

Publication frequency: 1 issue per year

Keywords:

Oligonucleotide composition,

Batch-Learning Self-Organizing Map (BLSOM),

Unsupervised explainable machine learning,

Time-series trend

© 2021 Takashi Abe, Ryuki Furukawa, Yuki Iwasaki, Toshimichi Ikemura, published by Ubiquity Press
This work is licensed under the Creative Commons Attribution 4.0 License.

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