Predicting anticancer hyperfoods with graph convolutional networks.
Guadalupe GonzalezShunwang GongIvan LaponogovMichael BronsteinKirill VeselkovPublished in: Human genomics (2021)
We introduce an end-to-end graph convolutional model to predict cancer-beating molecules within food. The introduced model outperforms the existing baseline approach, and shows interpretability, paving the way to the future of a personalized nutritional science approach allowing the development of nutrition strategies for cancer prevention and/or therapeutics.