  The emerging research shows that lncRNAs are associated with a series of
complex human diseases. However, most of the existing methods have limitations
in identifying nonlinear lncRNA-disease associations (LDAs), and it remains a
huge challenge to predict new LDAs. Therefore, the accurate identification of
LDAs is very important for the warning and treatment of diseases. In this work,
multiple sources of biomedical data are fully utilized to construct
characteristics of lncRNAs and diseases, and linear and nonlinear
characteristics are effectively integrated. Furthermore, a novel deep learning
model based on graph attention automatic encoder is proposed, called HGATELDA.
To begin with, the linear characteristics of lncRNAs and diseases are created
by the miRNA-lncRNA interaction matrix and miRNA-disease interaction matrix.
Following this, the nonlinear features of diseases and lncRNAs are extracted
using a graph attention auto-encoder, which largely retains the critical
information and effectively aggregates the neighborhood information of nodes.
In the end, LDAs can be predicted by fusing the linear and nonlinear
characteristics of diseases and lncRNA. The HGATELDA model achieves an
impressive AUC value of 0.9692 when evaluated using a 5-fold cross-validation
indicating its superior performance in comparison to several recent prediction
models. Meanwhile, the effectiveness of HGATELDA in identifying novel LDAs is
further demonstrated by case studies. the HGATELDA model appears to be a viable
computational model for predicting LDAs.
