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

Publication Type : Conference Paper

Publisher : SpringerLink

Source : Communications in Computer and Information Science

Url :

Campus : Coimbatore

School : School of Artificial Intelligence - Coimbatore

Year : 2023

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.

Cite this Research Publication : 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.

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