Identification of essential genes associated with SARS-CoV-2 infection as potential drug target candidates with machine learning algorithms.
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Taheri G, Habibi M
Sci Rep 13 (1) 15141 [2023-09-13; online 2023-09-13]
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 .
Category: Genomics & transcriptomics
PubMed 37704748
DOI 10.1038/s41598-023-42127-9
Crossref 10.1038/s41598-023-42127-9
pmc: PMC10499814
pii: 10.1038/s41598-023-42127-9