CNN-Based Detection of SARS-CoV-2 Variants Using Spike Protein Hydrophobicity

Mohammad Jamhuri, Mohammad Isa Irawan, Imam Mukhlash, Ni Nyoman Tri Puspaningsih

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

In the fight against the COVID-19 pandemic, it is crucial to quickly and accurately identify SARS-Co V-2 variants due to their ever-changing nature. In this study, we introduce a novel approach utilizing Convolutional Neural Networks (CNN) to classify the spike protein sequences of the virus, achieving an outstanding accuracy rate of 99.75%. For this method, we transformed a range of spike protein sequences, representing diverse SARS-CoV-2 variants, into images using the Kyte and Doolittle method to align with CNN input features. Comparative analyses with existing methodologies demonstrate the superior efficiency of our approach in terms of speed and precision. Such advancements in diagnostics play a fundamental role in shaping timely and informed public health strategies.

Original languageEnglish
Title of host publication2023 1st International Conference on Advanced Engineering and Technologies, ICONNIC 2023 - Proceeding
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages207-212
Number of pages6
ISBN (Electronic)9798350306484
DOIs
Publication statusPublished - 2023
Event1st International Conference on Advanced Engineering and Technologies, ICONNIC 2023 - Kediri, Indonesia
Duration: 14 Oct 2023 → …

Publication series

Name2023 1st International Conference on Advanced Engineering and Technologies, ICONNIC 2023 - Proceeding

Conference

Conference1st International Conference on Advanced Engineering and Technologies, ICONNIC 2023
Country/TerritoryIndonesia
CityKediri
Period14/10/23 → …

Keywords

  • Covid-19
  • Deep learning
  • Kyte and Doolittle
  • Spike protein sequences
  • Virus variants

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