TY - GEN
T1 - Graph Neural Networks for Predicting Protein Functions
AU - Ioannidis, Vassilis N.
AU - Marques, Antonio G.
AU - Giannakis, Georgios B.
PY - 2019/12
Y1 - 2019/12
N2 - 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.
AB - 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.
KW - Deep neural networks
KW - graph neural networks
KW - graph signals
KW - multi-relational graphs
KW - protein networks
UR - http://www.scopus.com/inward/record.url?scp=85082399045&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85082399045&partnerID=8YFLogxK
U2 - 10.1109/CAMSAP45676.2019.9022646
DO - 10.1109/CAMSAP45676.2019.9022646
M3 - Conference contribution
T3 - 2019 IEEE 8th International Workshop on Computational Advances in Multi-Sensor Adaptive Processing, CAMSAP 2019 - Proceedings
SP - 221
EP - 225
BT - 2019 IEEE 8th International Workshop on Computational Advances in Multi-Sensor Adaptive Processing, CAMSAP 2019 - Proceedings
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 8th IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing, CAMSAP 2019
Y2 - 15 December 2019 through 18 December 2019
ER -
