Graph Attentional Autoencoder for Anticancer Hyperfood Prediction

01/16/2020
by   Guadalupe Gonzalez, et al.
14

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.

READ FULL TEXT

page 1

page 2

page 3

page 4

page 5

10/27/2019

Pre-train and Learn: Preserve Global Information for Graph Neural Networks

Graph neural networks (GNNs) have shown great power in learning on attri...
11/23/2020

AutoGraph: Automated Graph Neural Network

Graphs play an important role in many applications. Recently, Graph Neur...
07/24/2020

Hierachial Protein Function Prediction with Tails-GNNs

Protein function prediction may be framed as predicting subgraphs (with ...
07/19/2020

EPGAT: Gene Essentiality Prediction With Graph Attention Networks

The identification of essential genes/proteins is a critical step toward...
04/20/2022

Graph neural networks and attention-based CNN-LSTM for protein classification

This paper focuses on three critical problems on protein classification....
06/12/2019

Graph Embedding on Biomedical Networks: Methods, Applications, and Evaluations

Motivation: Graph embedding learning which aims to automatically learn l...
10/16/2020

Predicting Biomedical Interactions with Higher-Order Graph Convolutional Networks

Biomedical interaction networks have incredible potential to be useful i...