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Exploring the Lethality of Human-Adapted Coronavirus Through Alignment-Free Machine Learning Approaches Using Genomic
Rui Yin1,2, Zihan Luo3, Chee Keong Kwoh1
1School of Computer Science and Engineering, Nanyang Technological University, 50 Nanyang Avenue, 639798, Singapore.
Current Genomics
|April 7, 2022
Summary
This study introduces a machine learning framework to predict coronavirus lethality using genomic sequences, achieving 96.7% accuracy. This tool offers rapid, real-time lethality estimation for novel coronavirus strains.
Area of Science:
- Virology
- Genomics
- Machine Learning
Background:
- The global spread of novel coronaviruses, including SARS-CoV, MERS-CoV, and SARS-CoV-2, necessitates rapid assessment of viral lethality.
- Understanding coronavirus characteristics is crucial for managing infectious diseases and developing effective treatments.
Purpose of the Study:
- To develop an accurate and rapid method for predicting the lethality of human-adapted coronaviruses.
- To provide a tool for real-time estimation of viral toxicity for emerging coronavirus strains.
Main Methods:
- An alignment-free computational framework utilizing machine learning and digital signal processing on genomic sequences.
- Testing of six different feature transformation and machine learning algorithms on existing coronavirus strains.
Main Results:
- Achieved an average prediction accuracy of 96.7% on SARS-CoV, MERS-CoV, and SARS-CoV-2 datasets.
- Demonstrated high prediction performance using only RNA sequences, without requiring genome annotations or specialized biological knowledge.
Conclusions:
- The developed framework offers a reliable method for real-time estimation of viral lethality in novel human coronavirus strains.
- This approach facilitates prompt assessment of emerging viral threats.
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