  Biomedical information graphs are crucial for interaction discovering of
biomedical information in modern age, such as identification of multifarious
molecular interactions and drug discovery, which attracts increasing interests
in biomedicine, bioinformatics, and human healthcare communities. Nowadays,
more and more graph neural networks have been proposed to learn the entities of
biomedical information and precisely reveal biomedical molecule interactions
with state-of-the-art results. These methods remedy the fading of features from
a far distance but suffer from remedying such problem at the expensive cost of
redundant memory and time. In our paper, we propose a novel Residual Message
Graph Convolution Network (ResMGCN) for fast and precise biomedical interaction
prediction in a different idea. Specifically, instead of enhancing the message
from far nodes, ResMGCN aggregates lower-order information with the next round
higher information to guide the node update to obtain a more meaningful node
representation. ResMGCN is able to perceive and preserve various messages from
the previous layer and high-order information in the current layer with least
memory and time cost to obtain informative representations of biomedical
entities. We conduct experiments on four biomedical interaction network
datasets, including protein-protein, drug-drug, drug-target, and gene-disease
interactions, which demonstrates that ResMGCN outperforms previous
state-of-the-art models while achieving superb effectiveness on both storage
and time.

Machine Learning;Artificial Intelligence;Molecular Networks