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Abstract
This manuscript explores the intersection of computational molecular biology and data science in the analysis of SARS-CoV-2 genetic sequencing data. Leveraging advanced computational methods, we present a comprehensive examination of COVID-19 mutations, combining molecular insights with data-driven approaches. The integration of these disciplines contributes to a deeper understanding of the virus's genomic landscape and its implications for public health.
Genomic sequences of SARS-CoV-2 were subjected to cutting-edge computational molecular biology techniques, including sequence alignment, variant calling, and molecular dynamics simulations. These methods provided a detailed examination of genetic variations and structural consequences associated with COVID-19 mutations. Concurrently, data science methodologies were employed for feature extraction, engineering, and the development of predictive models to discern functional outcomes.
Molecular dynamics simulations revealed distinct structural changes correlated with specific mutations, particularly in the spike protein's receptor-binding domain. Supervised machine learning models demonstrated high accuracy in predicting the functional impact of mutations, emphasizing key genomic positions crucial for viral fitness and transmissibility. Network analysis unveiled central genes and pathways influenced by mutations, providing insights into potential drug targets and therapeutic interventions.
The integration of computational molecular biology and data science represents a paradigm shift in our approach to understanding COVID-19 mutations. By combining molecular dynamics insights with predictive modeling and network analysis, this research contributes a holistic perspective on SARS-CoV-2 evolution. The multidisciplinary findings underscore the potential for targeted interventions and inform evidence-based public health strategies in the ongoing battle against the pandemic.
