  Background: The expanding research in the field of long non-coding
RNAs(lncRNAs) showed abnormal expression of lncRNAs in many complex diseases.
Accurately identifying lncRNA-disease association is essential in understanding
lncRNA functionality and disease mechanism. There are many machine learning
techniques involved in the prediction of lncRNA-disease association which use
different biological interaction networks and associated features. Feature
learning from the network structured data is one of the limiting factors of
machine learning-based methods. Graph neural network based techniques solve
this limitation by unsupervised feature learning. Deep belief networks (DBN)
are recently used in biological network analysis to learn the latent
representations of network features.
  Method: In this paper, we propose a DBN based lncRNA-disease association
prediction model (DBNLDA) from lncRNA, disease and miRNA interactions. The
architecture contains three major modules-network construction, DBN based
feature learning and neural network-based prediction. First, we constructed
three heterogeneous networks such as lncRNA-miRNA similarity (LMS),
disease-miRNA similarity (DMS) and lncRNA-disease association (LDA) network.
From the node embedding matrices of similarity networks, lncRNA-disease
representations were learned separately by two DBN based subnetworks. The joint
representation of lncRNA-disease was learned by a third DBN from outputs of the
two subnetworks mentioned. This joint feature representation was used to
predict the association score by an ANN classifier.
  Result: The proposed method obtained AUC of 0.96 and AUPR of 0.967 when
tested against standard dataset used by the state-of-the-art methods. Analysis
on breast, lung and stomach cancer cases also affirmed the effectiveness of
DBNLDA in predicting significant lncRNA-disease associations.
