  Graphs can be used to effectively represent complex data structures. Learning
these irregular data in graphs is challenging and still suffers from shallow
learning. Applying deep learning on graphs has recently showed good performance
in many applications in social analysis, bioinformatics etc. A message passing
graph convolution network is such a powerful method which has expressive power
to learn graph structures. Meanwhile, circRNA is a type of non-coding RNA which
plays a critical role in human diseases. Identifying the associations between
circRNAs and diseases is important to diagnosis and treatment of complex
diseases. However, there are limited number of known associations between them
and conducting biological experiments to identify new associations is time
consuming and expensive. As a result, there is a need of building efficient and
feasible computation methods to predict potential circRNA-disease associations.
In this paper, we propose a novel graph convolution network framework to learn
features from a graph built with multi-source similarity information to predict
circRNA-disease associations. First we use multi-source information of circRNA
similarity, disease and circRNA Gaussian Interaction Profile (GIP) kernel
similarity to extract the features using first graph convolution. Then we
predict disease associations for each circRNA with second graph convolution.
Proposed framework with five-fold cross validation on various experiments shows
promising results in predicting circRNA-disease association and outperforms
other existing methods.
