Predicting LncRNA-Disease Association Based on Generative Adversarial Network

Abstract

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). Method: 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 function. 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 semi-supervised 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.

Recent Advances in Generative Adversarial Networks for Gene Expression Data: A Comprehensive Review

“…The predictive models for disease associations proposed by Du et al [61], Yan et al [62], and Wang et al [63], namely LDA-GAN, GANCDA, and SGANRDA, respectively, harness the strengths of GANs in data augmentation and complex distribution modeling. Limited data availability and complex data structures are common challenges in disease association prediction.…”

MNNMDA: Predicting human microbe-disease association via a method to minimize matrix nuclear norm

“…Recently, machine learning technologies have been widely used and achieved remarkable performances in association predictions, such as lncRNA-disease association prediction [8] , [9] , [10] , [11] , [12] , [13] , drug repositioning [14] , [15] , [16] , [17] , [18] , [19] , miRNA-disease association prediction [20] , [21] , [22] , [23] and so on. Simultaneously, many computational methods have been developed to help identify the relationship between microbes and diseases.…”

Graph Reasoning Method Based on Affinity Identification and Representation Decoupling for Predicting lncRNA-Disease Associations

“…LNPM designed a lineal neighborhood propagation-based method to exploit the graph structure to enhance the feature representations of nodes. Other GNN methods, including graph attention networks, , graph generative adversarial networks, , graph self-encoders, , and graph convolutional networks have as well been used to develop computational prediction models. In addition, the graph reasoning methods like the network distance and graphlet interaction were proposed for association prediction.…”