AutoCoV: tracking the early spread of COVID-19 in terms of the spatial and temporal patterns from embedding space by K-mer based deep learning.

Journal: BMC bioinformatics
Published Date: Apr 25, 2022

Abstract

BACKGROUND: The widely spreading coronavirus disease (COVID-19) has three major spreading properties: pathogenic mutations, spatial, and temporal propagation patterns. We know the spread of the virus geographically and temporally in terms of statistics, i.e., the number of patients. However, we are yet to understand the spread at the level of individual patients. As of March 2021, COVID-19 is wide-spread all over the world with new genetic variants. One important question is to track the early spreading patterns of COVID-19 until the virus has got spread all over the world.

Authors

  • Inyoung Sung
    Interdisciplinary Program in Bioinformatics, Seoul National University, Seoul, Republic of Korea.
  • Sangseon Lee
    Department of Computer Science and Engineering, Seoul National University, Seoul, Republic of Korea.
  • Minwoo Pak
    Department of Computer Science and Engineering, Seoul National University, Seoul, Republic of Korea.
  • Yunyol Shin
    Department of Computer Science and Engineering, Seoul National University, Seoul, Republic of Korea.
  • Sun Kim
    National Center for Biotechnology Information, National Library of Medicine, National Institutes of Health, Bethesda, 20894, MD, USA. sun.kim@nih.gov.

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