ArticleHuman genomics2021
Network machine learning maps phytochemically rich "Hyperfoods" to fight COVID-19.
Article in Human genomics, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.
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The trial behind it
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Who cites it
14 citing papers in PubMed.
- Unlocking the Power of Plant-Derived Natural Products: Therapeutic Benefits for Cognitive Health and Neuropsychiatric Symptoms in Dementia-Related Diseases.Plants (Basel, Switzerland) · 2026Review
- Identifying nutraceutical targets to treat polycystic ovary syndrome using graph representation learning.npj women's health · 2025Article
- Possible roles of phytochemicals with bioactive properties in the prevention of and recovery from COVID-19.Frontiers in nutrition · 2024Article
- Crosstalk with lung fibroblasts shapes the growth and therapeutic response of mesothelioma cells.Cell death & disease · 2023Article
- Genomic-driven nutritional interventions for radiotherapy-resistant rectal cancer patient.Scientific reports · 2023Article
- Alzheimer's disease: using gene/protein network machine learning for molecule discovery in olive oil.Human genomics · 2023Article
- Protein structure-based in-silico approaches to drug discovery: Guide to COVID-19 therapeutics.Molecular aspects of medicine · 2023Review
- Dysbiosis in gastrointestinal pathophysiology: Role of the gut microbiome in Gulf War Illness.Journal of cellular and molecular medicine · 2023Review
- Factors Modulating COVID-19: A Mechanistic Understanding Based on the Adverse Outcome Pathway Framework.Journal of clinical medicine · 2022Review
- Discovery of the key active compounds in Citri Reticulatae Pericarpium (Frontiers in pharmacology · 2022Article
- Digital Citizen Science for Responding to COVID-19 Crisis: Experiences from Iran.International journal of environmental research and public health · 2021Article
- Phytochemicals: Do They Belong on our Plate for Sustaining Healthspan?Food frontiers · 2021Article
- Artificial intelligence-driven drug repurposing and structural biology for SARS-CoV-2.Current research in pharmacology and drug discovery · 2021Review
- The Role of Artificial Intelligence in Fighting the COVID-19 Pandemic.Information systems frontiers : a journal of research and innovation · 2021Article
Corrections and comments
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Authors and funding
11 authors.
Funding
Abstract
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, harnessing the idle computational power of thousands of smartphones. Machine learning models were initially calibrated by demonstrating that the proposed method can predict anti-COVID-19 candidates among experimental and clinically approved drugs (5658 in total) targeting COVID-19 interactomics with the balanced classification accuracy of 80-85% in 5-fold cross-validated settings. This identified the most promising drug candidates that can be potentially "repurposed" against COVID-19 including common drugs used to combat cardiovascular and metabolic disorders, such as simvastatin, atorvastatin and metformin. A database of 7694 bioactive food-based molecules was run through the calibrated machine learning algorithm, which identified 52 biologically active molecules, from varied chemical classes, including flavonoids, terpenoids, coumarins and indoles predicted to target SARS-CoV-2-host interactome networks. This in turn was used to construct a "food map" with the theoretical anti-COVID-19 potential of each ingredient estimated based on the diversity and relative levels of candidate compounds with antiviral properties. We expect this in silico predicted food map to play an important role in future clinical studies of precision nutrition interventions against COVID-19 and other viral diseases.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.