Have a look at this amazing new Nature article:
HyperFoods: Machine intelligent mapping of cancer-beating molecules in foods
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.
What foods were identified as having strong anti-cancer activity?
Using a network-based machine learning method, we have shown that plant-based foods such as tea, carrot, celery, orange, grape, coriander, cabbage and dill contain the largest number of molecules with high anti-cancer likeness through exerting influence on molecular networks in a similar fashion to existing therapeutics.
There is no way you would find this out on your own -- and, furthermore, there may be other foods that interfere with the action of these listed.
Now imagine the same is also done for other major killers, like diabetes, heart disease, and dementia -- and also for slowing aging and extending longevity. And then imagine it also uses inputs from multiple biosensors injected in your body (where they constantly and permanently stream your vital stats), to work out the optimal diet for you. The system would maybe tell you, "eat more grapes for dinner, cut back on chicken"; and you wouldn't understand why, or what complex combinations of molecules were in those foods, and what "interactome networks" were being impacted. But you would notice the results: you would get healthier and healthier, and your aging would appear to slow way down or even reverse.
(It might even be possible to permanently alter your eating habits through neurofeedback, so that you always seek out food combinations to improve your health, no "reminders" required.)
The world will soon get a lot healthier!