Network machine learning maps phytochemically rich “Hyperfoods” to fight COVID-19

• Published in: Human genomics Vol. 15; no. 1; pp. 1 - 11
• Main Authors: Laponogov, Ivan, Gonzalez, Guadalupe, Shepherd, Madelen, Qureshi, Ahad, Veselkov, Dennis, Charkoftaki, Georgia, Vasiliou, Vasilis, Youssef, Jozef, Mirnezami, Reza, Bronstein, Michael, Veselkov, Kirill
• Format: Journal Article
• Language: English
• Published: England BioMed Central 02.01.2021; BMC
• Subjects: Antiviral; Antiviral activity; Antiviral agents; Antiviral drugs; Atorvastatin; Biological activity; Computer applications; Coronaviruses; COVID-19; COVID-19 - diet therapy; COVID-19 - virology; Databases, Factual; Drug delivery; Drug development; Drug repositioning; Flavonoids; Food; Functional Food; Gene mapping; Genes, Viral; Humans; Kinases; Learning algorithms; Machine Learning; Metabolic disorders; Metformin; Primary Research; SARS-CoV-2; SARS-CoV-2 - genetics; SARS-CoV-2 - isolation & purification; Severe acute respiratory syndrome coronavirus 2; Simvastatin; Viral diseases; Viruses; COVID-19; Gene-gene networks; SARS-CoV-2; Interactomics; Machine learning; Drug repositioning; Antiviral; Food

In this paper, we introduce a network machine learning method to identify potential bioactive anti-COVID-19 molecules in foods based on their capacity to target the SARS-CoV-2-host gene-gene (protein-protein) interactome. Our analyses were performed using a supercomputing DreamLab App platform, harn...