Name
Abstract | ||
|---|---|---|
Learning the functions associated with a protein is essential to gaining insights for disease diagnostics, medical treatment, and human biology. In this paper, protein function prediction is posed as a semi-supervised learning task over multi-relational graphs, and it is tackled using a graph neural network (GNN) approach. The novel GNN architecture employs multi-relational graphs and weighs the influence of the different relations via learnable parameters. The ultimate goal is to design a powerful learning architecture able to: discover complex and highly nonlinear data associations, combine (and select) multiple types of relations, and scale gracefully with respect to the size of the graph. Numerical tests with protein networks corroborate the performance gains relative to state-of-the-art alternatives. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1109/CAMSAP45676.2019.9022646 | 2019 IEEE 8th International Workshop on Computational Advances in Multi-Sensor Adaptive Processing (CAMSAP) |
Keywords | DocType | ISBN |
Deep neural networks,protein networks,graph neural networks,graph signals,multi-relational graphs | Conference | 978-1-7281-5550-0 |
Citations | PageRank | References |
0 | 0.34 | 6 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Vassilis N. Ioannidis | 1 | 14 | 7.34 |
| Antonio Marqués | 2 | 28 | 4.94 |
| G. B. Giannakis | 3 | 11464 | 1206.47 |