HyperFoods: Machine intelligent mapping of cancer-beating molecules in foods

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.

Figure 1

Schematic diagram of the overall workflow.

Figure 2

Relevant genes and pathways derived from machine learning models for prediction of anti-cancer therapeutics tested in human trials. Individual node size corresponds to the relative discriminating capacity of a given gene-encoded protein and node color illustrates shared biological pathway functionality.

Figure 3

Hierarchical classification of the top 110 predicted cancer-beating molecules in food with anti-cancer drug likeness of >0.7.

Figure 4

The contained profiles of compounds within selective foods, which were highly likely to be effective in fighting cancer. Each node in the figure denotes a particular food item and node size in each case is proportional to the number of CBMs. The link between nodes reflects the pairwise correlation profile of CBMs in foods, thus the clusters of foods illustrate molecular commonality between them.