Predicting lncRNA-disease associations based on heterogeneous graph convolutional generative adversarial network

作者：Zhonghao Lu, Hua Zhong, Lin Tang, Jing Luo, Wei Zhou, Lin Liu · 发表于：PLoS Computational Biology · 年份：2023 · DOI：10.1371/journal.pcbi.1011634 · 被引用次数：19 · 研究领域：Cancer-related molecular mechanisms research

There is a growing body of evidence indicating the crucial roles that long non-coding RNAs (lncRNAs) play in the development and progression of various diseases, including cancers, cardiovascular diseases, and neurological disorders. However, accurately predicting potential lncRNA-disease associations remains a challenge, as existing methods have limitations in extracting heterogeneous association information and handling sparse and unbalanced data. To address these issues, we propose a novel computational method, called HGC-GAN, which combines heterogeneous graph convolutional neural networks (GCN) and generative adversarial networks (GAN) to predict potential lncRNA-disease associations. Specifically, we construct a lncRNA-miRNA-disease heterogeneous network by integrating multiple association data and sequence information. The GCN-based generator is then employed to aggregate neighbor information of nodes and obtain node embeddings, which are used to predict lncRNA-disease associations. Meanwhile, the GAN-based discriminator is trained to distinguish between real and fake lncRNA-disease associations generated by the generator, enabling the generator to improve its ability to generate accurate lncRNA-disease associations gradually. Our experimental results demonstrate that HGC-GAN performs better in predicting potential lncRNA-disease associations, with AUC and AUPR values of 0.9591 and 0.9606, respectively, under 10-fold cross-validation. Moreover, our case study further c...