Graph Attentional Autoencoder for Anticancer Hyperfood Prediction
This work addresses the discovery of anticancer food compounds for potential disease-beating diet design, but it appears incremental as it builds on existing graph neural network approaches.
The paper tackled the problem of predicting anticancer compounds in food by formulating it as a graph classification task, and the result was that their Graph Attentional Autoencoder method outperformed baseline and state-of-the-art models.
Recent research efforts have shown the possibility to discover anticancer drug-like molecules in food from their effect on protein-protein interaction networks, opening a potential pathway to disease-beating diet design. We formulate this task as a graph classification problem on which graph neural networks (GNNs) have achieved state-of-the-art results. However, GNNs are difficult to train on sparse low-dimensional features according to our empirical evidence. Here, we present graph augmented features, integrating graph structural information and raw node attributes with varying ratios, to ease the training of networks. We further introduce a novel neural network architecture on graphs, the Graph Attentional Autoencoder (GAA) to predict food compounds with anticancer properties based on perturbed protein networks. We demonstrate that the method outperforms the baseline approach and state-of-the-art graph classification models in this task.