tetano
Editor, Senior Moderator
Sci Rep
. 2023 Sep 13;13(1):15141.
doi: 10.1038/s41598-023-42127-9. Identification of essential genes associated with SARS-CoV-2 infection as potential drug target candidates with machine learning algorithms
Golnaz Taheri[SUP] #[/SUP][SUP] 1 2 [/SUP], Mahnaz Habibi[SUP] #[/SUP][SUP] 3 [/SUP]
Affiliations
Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) requires the fast discovery of effective treatments to fight this worldwide concern. Several genes associated with the SARS-CoV-2, which are essential for its functionality, pathogenesis, and survival, have been identified. These genes, which play crucial roles in SARS-CoV-2 infection, are considered potential therapeutic targets. Developing drugs against these essential genes to inhibit their regular functions could be a good approach for COVID-19 treatment. Artificial intelligence and machine learning methods provide powerful infrastructures for interpreting and understanding the available data and can assist in finding fast explanations and cures. We propose a method to highlight the essential genes that play crucial roles in SARS-CoV-2 pathogenesis. For this purpose, we define eleven informative topological and biological features for the biological and PPI networks constructed on gene sets that correspond to COVID-19. Then, we use three different unsupervised learning algorithms with different approaches to rank the important genes with respect to our defined informative features. Finally, we present a set of 18 important genes related to COVID-19. Materials and implementations are available at: https://github.com/MahnazHabibi/Gene_analysis .
. 2023 Sep 13;13(1):15141.
doi: 10.1038/s41598-023-42127-9. Identification of essential genes associated with SARS-CoV-2 infection as potential drug target candidates with machine learning algorithms
Golnaz Taheri[SUP] #[/SUP][SUP] 1 2 [/SUP], Mahnaz Habibi[SUP] #[/SUP][SUP] 3 [/SUP]
Affiliations
- PMID: 37704748
- DOI: 10.1038/s41598-023-42127-9
Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) requires the fast discovery of effective treatments to fight this worldwide concern. Several genes associated with the SARS-CoV-2, which are essential for its functionality, pathogenesis, and survival, have been identified. These genes, which play crucial roles in SARS-CoV-2 infection, are considered potential therapeutic targets. Developing drugs against these essential genes to inhibit their regular functions could be a good approach for COVID-19 treatment. Artificial intelligence and machine learning methods provide powerful infrastructures for interpreting and understanding the available data and can assist in finding fast explanations and cures. We propose a method to highlight the essential genes that play crucial roles in SARS-CoV-2 pathogenesis. For this purpose, we define eleven informative topological and biological features for the biological and PPI networks constructed on gene sets that correspond to COVID-19. Then, we use three different unsupervised learning algorithms with different approaches to rank the important genes with respect to our defined informative features. Finally, we present a set of 18 important genes related to COVID-19. Materials and implementations are available at: https://github.com/MahnazHabibi/Gene_analysis .