Subgraph Topology and Dynamic Graph Topology Enhanced Graph Learning and Pairwise Feature Context Relationship Integration for Predicting Disease-Related miRNAs

As an increasing number of microRNAs (miRNAs) have become
biomarkers
of various human diseases, prediction of the candidate disease-related
miRNAs is helpful for facilitating the early diagnosis of diseases.
Most of the recent prediction models concentrated on learning of the
features from the heterogeneous graph composed of miRNAs and diseases.
However, they failed to fully exploit the subgraph structures consisting
of multiple miRNA and disease nodes, and they also did not completely
integrate the context relationships among the pairwise features. We
proposed a prediction model, SFPred, to integrate and encode the local
topologies from neighborhood subgraphs, the dynamically evolved heterogeneous
graph topology, and the context among pairwise features. First, the
importance of an miRNA (disease) node to another node is formulated
according to the subgraphs composed of their neighbors. Second, the
features of each miRNA (disease) node continuously change when the
graph encoding gradually deepens for the miRNA-disease heterogeneous
network. A strategy based on multi-layer perceptron (MLP) is designed
to estimate the edge weights according to the changed node features
and form the dynamic graph topology. Third, considering the context
relationships among the features of a pair of miRNA and disease nodes,
a context relationship sensitive transformer is constructed to integrate
these relationships. Finally, since the previous encoding layer of
the transformer contains more detailed features of the pairwise, we
present a multiperspective residual strategy to supplement the detailed
features to the following encoding layer from the channel perspective
and the feature one, respectively. The extensive experiments confirmed
that SFPred outperforms eight state-of-the-art methods for the prediction
of miRNA-disease associations, and the ablation experiments validate
the effectiveness of the proposed innovations. The recall rates for
the top-ranked candidate miRNAs related to the diseases and the case
studies on three diseases indicate SFPred’s ability in screening
the reliable candidates for subsequent biological experiments.