Learning Association Characteristics by Dynamic Hypergraph and Gated Convolution Enhanced Pairwise Attributes for Prediction of Disease-Related lncRNAs

As
the long non-coding RNAs (lncRNAs) play important roles during
the incurrence and development of various human diseases, identifying
disease-related lncRNAs can contribute to clarifying the pathogenesis
of diseases. Most of the recent lncRNA-disease association prediction
methods utilized the multi-source data about the lncRNAs and diseases.
A single lncRNA may participate in multiple disease processes, and
multiple lncRNAs usually are involved in the same disease process
synergistically. However, the previous methods did not completely
exploit the biological characteristics to construct the informative
prediction models. We construct a prediction model based on adaptive hypergraph and gated convolution for lncRNA-disease association prediction (AGLDA), to embed and encode
the biological characteristics about lncRNA–disease associations,
the topological features from the entire heterogeneous graph perspective,
and the gated enhanced pairwise features. First, the strategy for
constructing hyperedges is designed to reflect the biological characteristic
that multiple lncRNAs are involved in multiple disease processes.
Furthermore, each hyperedge has its own biological perspective, and
multiple hyperedges are beneficial for revealing the diverse relationships
among multiple lncRNAs and diseases. Second, we encode the biological
features of each lncRNA (disease) node using a strategy based on dynamic
hypergraph convolutional networks. The strategy may adaptively learn
the features of the hyperedges and formulate the dynamically evolved
hypergraph topological structure. Third, a group convolutional network
is established to integrate the entire heterogeneous topological structure
and multiple types of node attributes within an lncRNA–disease–miRNA
graph. Finally, a gated convolutional strategy is proposed to enhance
the informative features of the lncRNA–disease node pairs.
The comparison experiments indicate that AGLDA outperforms seven advanced
prediction methods. The ablation studies confirm the effectiveness
of major innovations, and the case studies validate AGLDA’s
ability in application for discovering potential disease-related lncRNA
candidates.