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Image processing unravels the evolutionary pattern of SARS-CoV-2 against SARS and MERS through position-based pattern
Reza Ahsan1, Mohammad Reza Tahsili2, Faezeh Ebrahimi3
1Department of Computer Engineering, Qom Branch, Islamic Azad University, Qom, Iran.
Computers in Biology and Medicine
|May 18, 2021
Summary
Machine learning identified key genomic differences between SARS-COV-2, SARS, and MERS coronaviruses. The 3'UTR region accurately distinguished these viruses, offering insights into coronavirus pathogenicity.
Area of Science:
- Virology
- Genomics
- Machine Learning
Background:
- Coronaviruses, including SARS-COV-2, SARS, and MERS, exhibit varying pathogenicity and geographical spread.
- Understanding structural differences at the genomic level is crucial for explaining these variations.
Purpose of the Study:
- To investigate structural differences in the genomes of SARS-COV-2, SARS, and MERS coronaviruses.
- To identify genomic regions responsible for distinct pathogenicities.
Main Methods:
- Genomic and proteomic sequences were analyzed using attribute weighting models and polynomial datasets.
- 3'UTR and Spike (S) protein sequences were converted to binary images for analysis.
- Image processing and Convolutional Neural Networks (CNN) were employed.
Main Results:
- Attribute weighting models highlighted significant differences in the terminal nucleotide sequences (3'UTR).
- Machine learning algorithms achieved 100% accuracy in classifying coronaviruses based on the 3'UTR region.
- CNN analysis of Spike (S) proteins showed lower predictive accuracy (0.48%).
Conclusions:
- The 3'UTR region contains critical genomic distinctions among SARS-COV-2, SARS, and MERS.
- Sequential genomic differences, particularly in the 3'UTR, correlate with coronavirus pathogenicity.
- This study provides a foundation for understanding SARS-COV-2's high pathogenicity.
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