Predicting anticancer hyperfoods with graph convolutional networks.

ABSTRACT:

Background

Recent efforts in the field of nutritional science have allowed the discovery of disease-beating molecules within foods based on the commonality of bioactive food molecules to FDA-approved drugs. The pioneering work in this field used an unsupervised network propagation algorithm to learn the systemic-wide effect on the human interactome of 1962 FDA-approved drugs and a supervised algorithm to predict anticancer therapeutics using the learned representations. Then, a set of bioactive molecules within foods was fed into the model, which predicted molecules with cancer-beating potential.The employed methodology consisted of disjoint unsupervised feature generation and classification tasks, which can result in sub-optimal learned drug representations with respect to the cla

SUBMITTER: Gonzalez G 

PROVIDER: S-EPMC8182908 | biostudies-literature | 2021 Jun

REPOSITORIES: biostudies-literature