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@article{Veselkov:2019:10.1038/s41598-019-45349-y,
author = {Veselkov, K and Gonzalez, Pigorini G and Aljifri, S and Galea, D and Mirnezami, R and Youssef, J and Bronstein, M and Laponogov, I},
doi = {10.1038/s41598-019-45349-y},
journal = {Scientific Reports},
title = {HyperFoods: Machine intelligent mapping of cancer-beating molecules in foods},
url = {http://dx.doi.org/10.1038/s41598-019-45349-y},
volume = {9},
year = {2019}
}

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TY  - JOUR
AB  - 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.
AU  - Veselkov,K
AU  - Gonzalez,Pigorini G
AU  - Aljifri,S
AU  - Galea,D
AU  - Mirnezami,R
AU  - Youssef,J
AU  - Bronstein,M
AU  - Laponogov,I
DO  - 10.1038/s41598-019-45349-y
PY  - 2019///
SN  - 2045-2322
TI  - HyperFoods: Machine intelligent mapping of cancer-beating molecules in foods
T2  - Scientific Reports
UR  - http://dx.doi.org/10.1038/s41598-019-45349-y
UR  - http://hdl.handle.net/10044/1/71519
VL  - 9
ER  -

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