CNN-based detection of SARS-CoV-2 variants using spike protein hydrophobicity

Jamhuri, Mohammad, Irawan, Mohammad Isa, Mukhlash, Imam and Puspaningsih, Ni Nyoman Tri (2023) CNN-based detection of SARS-CoV-2 variants using spike protein hydrophobicity. Presented at The 2023 1st International Conference on Advanced Engineering and Technologies (ICONNIC), 13 Oct 2023, Kediri. (In Press)

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Abstract

In the fight against the COVID-19 pandemic, it is crucial to quickly and accurately identify SARS-CoV-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. Our research results showcase the potential of deep learning in tackling global health challenges and laying the groundwork for future innovations in virus diagnostics.

Item Type:	Conference (Paper)
Keywords:	deep learning; spike protein sequences; Covid-19; virus variants; Kyte and Doolittle
Subjects:	01 MATHEMATICAL SCIENCES > 0102 Applied Mathematics > 010202 Biological Mathematics 08 INFORMATION AND COMPUTING SCIENCES > 0801 Artificial Intelligence and Image Processing > 080199 Artificial Intelligence and Image Processing not elsewhere classified
Divisions:	Faculty of Mathematics and Sciences > Department of Mathematics
Depositing User:	Mr Jamhuri Mohammad
Date Deposited:	24 Oct 2023 11:19

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