@inproceedings{5400b5e261d447679c00cf2eafe546e2,
title = "Identifying New COVID-19 Variants from Spike Proteins Using Novelty Detection",
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.",
keywords = "COVID-19, Humans, Pandemics/prevention & control, SARS-CoV-2, Spike Glycoprotein, Coronavirus/metabolism",
author = "Sayantani Basu and Campbell, {Roy H}",
note = "Funding Information: This project has been funded by the Jump ARCHES endowment through the Health Care Engineering Systems Center. This work uses data from GISAID (https://www.gisaid.org). We would like to acknowledge all contributing laboratories that have contributed their COVID-19 sequence data to GISAID. Publisher Copyright: {\textcopyright} 2022 International Medical Informatics Association (IMIA) and IOS Press.; 18th World Congress on Medical and Health Informatics: One World, One Health - Global Partnership for Digital Innovation, MEDINFO 2021 ; Conference date: 02-10-2021 Through 04-10-2021",
year = "2022",
month = jun,
day = "6",
doi = "10.3233/SHTI220167",
language = "English (US)",
series = "Studies in Health Technology and Informatics",
publisher = "IOS Press BV",
pages = "694--698",
editor = "Paula Otero and Philip Scott and Martin, {Susan Z.} and Elaine Huesing",
booktitle = "MEDINFO 2021: One World, One Health – Global Partnership for Digital Innovation",
}