ArticleScientific reports2019
HyperFoods: Machine intelligent mapping of cancer-beating molecules in foods.
Article in Scientific reports, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT04983017 (Randomized, Double-Blinded, Placebo-Controlled Study With Immunotype Specific Dietary Supplements to Improve Inflammatory Age® by Edifice Health), which is not on this map. Cited by 31 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Randomized, Double-Blinded, Placebo-Controlled Study With Immunotype Specific Dietary Supplements to Improve Inflammatory Age® by Edifice Health
Who cites it
31 citing papers in PubMed, 73 citations in OpenAlex.
- Unlocking the Power of Plant-Derived Natural Products: Therapeutic Benefits for Cognitive Health and Neuropsychiatric Symptoms in Dementia-Related Diseases.Plants (Basel, Switzerland) · 2026Review
- From Cookbooks to Networks: A Framework for Comparing Multiethnic Ingredient Systems in Transylvania.Foods (Basel, Switzerland) · 2026Article
- Smartphones as Catalysts for Synergistic Nutrition: A New Era in Bioactive Detection, Personalization, and Food System Intelligence.Food science & nutrition · 2025Review
- From phytochemicals to recipes: health indications and culinary uses of herbs and spices.NPJ science of food · 2025Article
- Simplicity within biological complexity.Bioinformatics advances · 2025Article
- Identifying nutraceutical targets to treat polycystic ovary syndrome using graph representation learning.npj women's health · 2025Article
- Single-cell analysis identifies conserved features of immune dysfunction in simulated microgravity and spaceflight.Nature communications · 2024Article
- Article
- Advancements in Predictive Microbiology: Integrating New Technologies for Efficient Food Safety Models.International journal of microbiology · 2024Review
- 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
- Review
- Advances in the Potential Application of 3D Food Printing to Enhance Elderly Nutritional Dietary Intake.Foods (Basel, Switzerland) · 2023Review
- Dysbiosis in gastrointestinal pathophysiology: Role of the gut microbiome in Gulf War Illness.Journal of cellular and molecular medicine · 2023Review
- A strategy for healthy eating habits of daily fruits revisited: A metabolomics study.Current research in food science · 2023Article
- Graph representation learning in biomedicine and healthcare.Nature biomedical engineering · 2022Review
- A knowledge graph to interpret clinical proteomics data.Nature biotechnology · 2022Article
- Genesis and Mechanism of Some Cancer Types and an Overview on the Role of Diet and Nutrition in Cancer Prevention.Molecules (Basel, Switzerland) · 2022Review
- Interaction data are identifiable even across long periods of time.Nature communications · 2022Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
8 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Recent data indicate that up-to 30-40% of cancers can be prevented by dietary and lifestyle measures alone. Herein, we introduce a unique network-based machine learning platform to identify putative food-based cancer-beating molecules. These have been identified through their molecular biological network commonality with clinically approved anti-cancer therapies. A machine-learning algorithm of random walks on graphs (operating within the supercomputing DreamLab platform) was used to simulate drug actions on human interactome networks to obtain genome-wide activity profiles of 1962 approved drugs (199 of which were classified as "anti-cancer" with their primary indications). A supervised approach was employed to predict cancer-beating molecules using these 'learned' interactome activity profiles. The validated model performance predicted anti-cancer therapeutics with classification accuracy of 84-90%. A comprehensive database of 7962 bioactive molecules within foods was fed into the model, which predicted 110 cancer-beating molecules (defined by anti-cancer drug likeness threshold of >70%) with expected capacity comparable to clinically approved anti-cancer drugs from a variety of chemical classes including flavonoids, terpenoids, and polyphenols. This in turn was used to construct a 'food map' with anti-cancer potential of each ingredient defined by the number of cancer-beating molecules found therein. Our analysis underpins the design of next-generation cancer preventative and therapeutic nutrition strategies.
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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.