Mask-Guided Target Node Feature Learning and Dynamic Detailed Feature Enhancement for lncRNA-Disease Association Prediction

Identifying new relevant long noncoding RNAs (lncRNAs)
for various
human diseases can facilitate the exploration of the causes and progression
of these diseases. Recently, several graph inference methods have
been proposed to predict disease-related lncRNAs by exploiting the
topological structure and node attributes within graphs. However,
these methods did not prioritize the target lncRNA and disease nodes
over auxiliary nodes like miRNA nodes, potentially limiting their
ability to fully utilize the features of the target nodes. We propose
a new method, mask-guided target node feature learning and dynamic
detailed feature enhancement for lncRNA-disease association prediction
(MDLD), to enhance node feature learning for improved lncRNA-disease
association prediction. First, we designed a heterogeneous graph masked
transformer autoencoder to guide feature learning, focusing more on
the features of target lncRNA (disease) nodes. The target nodes were
increasingly masked as training progressed, which helps develop a
more robust prediction model. Second, we developed a graph convolutional
network with dynamic residuals (GCNDR) to learn and integrate the
heterogeneous topology and features of all lncRNA, disease, and miRNA
nodes. GCNDR employs an interlayer residual strategy and a residual
evolution strategy to mitigate oversmoothing caused by multilayer
graph convolution. The interlayer residual strategy estimates the
importance of node features learned in the previous GCN encoding layer
for nodes in the current encoding layer. Additionally, since there
are dependencies in the importance of features of individual lncRNA
(disease, miRNA) nodes across multiple encoding layers, a gated recurrent
unit-based strategy is proposed to encode these dependencies. Finally,
we designed a perspective-level attention mechanism to obtain more
informative features of lncRNA and disease node pairs from the perspectives
of mask-enhanced and dynamic-enhanced node features. Cross-validation
experimental results demonstrated that MDLD outperformed 10 other
state-of-the-art prediction methods. Ablation experiments and case
studies on candidate lncRNAs for three diseases further proved the
technical contributions of MDLD and its capability to discover disease-related
lncRNAs.