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10.1016/j.patter.2021.100396

http://scihub22266oqcxt.onion/10.1016/j.patter.2021.100396
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suck abstract from ncbi


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pmid34778851      Patterns+(N+Y) 2022 ; 3 (1): 100396
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  • Machine learning and network medicine approaches for drug repositioning for COVID-19 #MMPMID34778851
  • Santos SS; Torres M; Galeano D; Sanchez MDM; Cernuzzi L; Paccanaro A
  • Patterns (N Y) 2022[Jan]; 3 (1): 100396 PMID34778851show ga
  • We present two machine learning approaches for drug repurposing. While we have developed them for COVID-19, they are disease-agnostic. The two methodologies are complementary, targeting SARS-CoV-2 and host factors, respectively. Our first approach consists of a matrix factorization algorithm to rank broad-spectrum antivirals. Our second approach, based on network medicine, uses graph kernels to rank drugs according to the perturbation they induce on a subnetwork of the human interactome that is crucial for SARS-CoV-2 infection/replication. Our experiments show that our top predicted broad-spectrum antivirals include drugs indicated for compassionate use in COVID-19 patients; and that the ranking obtained by our kernel-based approach aligns with experimental data. Finally, we present the COVID-19 repositioning explorer (CoREx), an interactive online tool to explore the interplay between drugs and SARS-CoV-2 host proteins in the context of biological networks, protein function, drug clinical use, and Connectivity Map. CoREx is freely available at: https://paccanarolab.org/corex/.
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