Background: Increasing research reveals that long non-coding RNAs (lncRNAs) play an
important role in various biological processes of human diseases. Nonetheless, only a handful of
lncRNA-disease associations have been experimentally verified. The study of lncRNA-disease association
prediction based on the computational model has provided a preliminary basis for biological experiments
to a great degree so as to cut down the huge cost of wet lab experiments.
Objective: This study aims to learn the real distribution of lncRNA-disease association from a limited
number of known lncRNA-disease association data. This paper proposes a new lncRNA-disease association
prediction model called LDA-GAN based on a Generative Adversarial Network (GAN).
Methods: Aiming at the problems of slow convergence rate, training instabilities, and unavailability of
discrete data in traditional GAN, LDA-GAN utilizes the Gumbel-softmax technology to construct a
differentiable process for simulating discrete sampling. Meanwhile, the generator and the discriminator
of LDA-GAN are integrated to establish the overall optimization goal based on the pairwise loss
Results: Experiments on standard datasets demonstrate that LDA-GAN achieves not only high stability
and high efficiency in the process of confrontation learning but also gives full play to the semisupervised
learning advantage of generative adversarial learning framework for unlabeled data, which
further improves the prediction accuracy of lncRNA-disease association. Besides, case studies show
that LDA-GAN can accurately generate potential diseases for several lncRNAs.
Conclusion: We introduce a generative adversarial model to identify lncRNA-disease associations.