Identifying New COVID-19 Variants from Spike Proteins Using Novelty Detection

Sayantani Basu, Roy H Campbell

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

The COVID-19 pandemic has caused millions of infections and deaths worldwide in an ongoing pandemic. With the passage of time, several variants of this virus have surfaced. Machine learning methods and algorithms have been very useful in understanding the virus and its implications so far. In this paper, we have studied a set of novelty detection algorithms and applied it to the problem of detecting COVID-19 variants. Our results show accuracies of 79.64% and 82.43% on the B.1.1.7 and B.1.351 variants respectively on ProtVec unaligned COVID-19 spike protein sequences using One Class SVM with fine-tuned parameters. We believe that a system for automated and timely detection of variants will help countries formulate mitigation measures and study remedies in terms of medicines and vaccines that can protect against the new variants.

Original languageEnglish (US)
Title of host publicationMEDINFO 2021: One World, One Health – Global Partnership for Digital Innovation
EditorsPaula Otero, Philip Scott, Susan Z. Martin, Elaine Huesing
PublisherIOS Press BV
Pages694-698
Number of pages5
ISBN (Electronic)9781643682648
DOIs
StatePublished - Jun 6 2022
Event18th World Congress on Medical and Health Informatics: One World, One Health - Global Partnership for Digital Innovation, MEDINFO 2021 - Virtual, Online
Duration: Oct 2 2021Oct 4 2021

Publication series

NameStudies in Health Technology and Informatics
Volume290
ISSN (Print)0926-9630
ISSN (Electronic)1879-8365

Conference

Conference18th World Congress on Medical and Health Informatics: One World, One Health - Global Partnership for Digital Innovation, MEDINFO 2021
CityVirtual, Online
Period10/2/2110/4/21

Keywords

  • COVID-19
  • Humans
  • Pandemics/prevention & control
  • SARS-CoV-2
  • Spike Glycoprotein, Coronavirus/metabolism

ASJC Scopus subject areas

  • Biomedical Engineering
  • Health Informatics
  • Health Information Management

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