TY - GEN
T1 - CNN-Based Detection of SARS-CoV-2 Variants Using Spike Protein Hydrophobicity
AU - Jamhuri, Mohammad
AU - Irawan, Mohammad Isa
AU - Mukhlash, Imam
AU - Puspaningsih, Ni Nyoman Tri
N1 - Publisher Copyright:
© 2023 IEEE.
PY - 2023
Y1 - 2023
N2 - 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.
AB - 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.
KW - Covid-19
KW - Deep learning
KW - Kyte and Doolittle
KW - Spike protein sequences
KW - Virus variants
UR - http://www.scopus.com/inward/record.url?scp=85190067610&partnerID=8YFLogxK
U2 - 10.1109/ICONNIC59854.2023.10467250
DO - 10.1109/ICONNIC59854.2023.10467250
M3 - Conference contribution
AN - SCOPUS:85190067610
T3 - 2023 1st International Conference on Advanced Engineering and Technologies, ICONNIC 2023 - Proceeding
SP - 207
EP - 212
BT - 2023 1st International Conference on Advanced Engineering and Technologies, ICONNIC 2023 - Proceeding
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 1st International Conference on Advanced Engineering and Technologies, ICONNIC 2023
Y2 - 14 October 2023
ER -
