The emerging research shows that lncRNA has crucial research value in a
series of complex human diseases. Therefore, the accurate identification of
lncRNA-disease associations (LDAs) is very important for the warning and
treatment of diseases. However, most of the existing methods have limitations
in identifying nonlinear LDAs, and it remains a huge challenge to predict new
LDAs. In this paper, a deep learning model based on a heterogeneous network and
convolutional neural network (CNN) is proposed for lncRNA-disease association
prediction, named HCNNLDA. The heterogeneous network containing the lncRNA,
disease, and miRNA nodes, is constructed firstly. The embedding matrix of a
lncRNA-disease node pair is constructed according to various biological
premises about lncRNAs, diseases, and miRNAs. Then, the low-dimensional feature
representation is fully learned by the convolutional neural network. In the
end, the XGBoot classifier model is trained to predict the potential LDAs.
HCNNLDA obtains a high AUC value of 0.9752 and AUPR of 0.9740 under the 5-fold
cross-validation. The experimental results show that the proposed model has
better performance than that of several latest prediction models. Meanwhile,
the effectiveness of HCNNLDA in identifying novel LDAs is further demonstrated
by case studies of three diseases. To sum up, HCNNLDA is a feasible calculation
model to predict LDAs.

通过建立一个基于异质网络和卷积神经网络（CNN）的深度学习模型，预测长链非编码 RNA - 疾病关联 (LDAs)，该模型名为 HCNNLDA。试验结果显示，HCNNLDA 比几个最新的预测模型具有更好的性能，并且通过三种疾病的案例研究进一步证明了 HCNNLDA 在识别新的 LDAs 方面的有效性。因此，HCNNLDA 是一个可行的用于预测 LDAs 的计算模型。