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Circ RNA Based Classification of SARS CoV-2, SARS CoV-1 and MERS-CoV Using Machine Learning

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Advances in Computing and Data Sciences (ICACDS 2023)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1848))

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Abstract

The SARS-CoV-2 virus has demonstrated its ability to adapt and spread in various environments, making it a challenging target for identification and prediction. While current studies in the field concentrates on utilization of transcriptome sequence classification to identify the virus, circular RNAs (circRNAs) have shown potential as a diagnostic marker for viral diseases. These single-stranded, covalently closed RNA molecules possess unique features such as RNA binding capacity and expression regulation, making it a promising source for potential biomarkers to create a new classification model. In this study, we propose a circRNA-based classification model utilizing the dna2vec algorithm to extract distributed representations of variable-length k-mers, combined with classical machine learning algorithms. The results demonstrate superior performance of the model, with Random Forest classifier achieving an accuracy of 99.99%, highlighting the efficacy of circRNA-based classification for SARS-CoV-2 identification and the potential of circRNAs as diagnostic markers for viral diseases.

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Authors and Affiliations

  1. Centre for Computational Engineering and Networking (CEN), Amrita School of Engineering, Coimbatore, Amrita Vishwa Vidyapeetham, Coimbatore, India

    M. Vinayak, Harishchander Anandaram, S. Sachin Kumar & K. P. Soman

Authors
  1. M. Vinayak
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  2. Harishchander Anandaram
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  3. S. Sachin Kumar
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  4. K. P. Soman
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Corresponding author

Correspondence to M. Vinayak .

Editor information

Editors and Affiliations

  1. Consilio Research Lab, Tallinn, Estonia

    Mayank Singh

  2. Jaypee University of Engineering and Technology, Guna, India

    Vipin Tyagi

  3. Jaypee University of Information Technology, Waknaghat, India

    P.K. Gupta

  4. Institute of Information Theory and Automation, Prague, Czech Republic

    Jan Flusser

  5. University of Ottawa, Ottawa, ON, Canada

    Tuncer Ören

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Vinayak, M., Anandaram, H., Sachin Kumar, S., Soman, K.P. (2023). Circ RNA Based Classification of SARS CoV-2, SARS CoV-1 and MERS-CoV Using Machine Learning. In: Singh, M., Tyagi, V., Gupta, P., Flusser, J., Ören, T. (eds) Advances in Computing and Data Sciences. ICACDS 2023. Communications in Computer and Information Science, vol 1848. Springer, Cham. https://doi.org/10.1007/978-3-031-37940-6_35

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  • DOI: https://doi.org/10.1007/978-3-031-37940-6_35

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-37939-0

  • Online ISBN: 978-3-031-37940-6

  • eBook Packages: Computer ScienceComputer Science (R0)

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