GCNCDA: A new method for predicting circRNA-disease associations based on Graph Convolutional Network Algorithm

Abstract

Numerous evidences indicate that Circular RNAs (circRNAs) are widely involved in the occurrence and development of diseases. Identifying the association between circRNAs and diseases plays a crucial role in exploring the pathogenesis of complex diseases and improving the diagnosis and treatment of diseases. However, due to the complex mechanisms between circRNAs and diseases, it is expensive and time-consuming to discover the new circRNA-disease associations by biological experiment. Therefore, there is increasingly urgent need for utilizing the computational methods to predict novel circRNA-disease associations. In this study, we propose a computational method called GCNCDA based on the deep learning Fast learning with Graph Convolutional Networks (FastGCN) algorithm to predict the potential disease-associated circRNAs. Specifically, the method first forms the unified descriptor by fusing disease semantic similarity information, disease and circRNA Gaussian Interaction Profile (GIP) kernel similarity information based on known circRNAdisease associations. The FastGCN algorithm is then used to objectively extract the highlevel features contained in the fusion descriptor. Finally, the new circRNA-disease associations are accurately predicted by the Forest by Penalizing Attributes (Forest PA) classifier. The 5-fold cross-validation experiment of GCNCDA achieved 91.2% accuracy with 92.78% sensitivity at the AUC of 90.90% on circR2Disease benchmark dataset. In comparison with different classifier models, feature extraction models and other state-of-the-art methods, GCNCDA shows strong competitiveness. Furthermore, we conducted case study experiments on diseases including breast cancer, glioma and colorectal cancer. The results showed that 16, 15 and 17 of the top 20 candidate circRNAs with the highest prediction scores were respectively confirmed by relevant literature and databases. These results suggest that GCNCDA can effectively predict potential circRNA-disease associations and provide highly credible candidates for biological experiments.

Double matrix completion for circRNA-disease association prediction

“…To prove the effectiveness of our method, we compared it with five state-of-the-art methods, that is, NCPCDA [ 11 ], PWCDA [ 7 ], iCircDA-MF [ 9 ], RWRKNN [ 14 ], and GCNCDA [ 15 ]. Among them, three methods (NCPCDA, PWCDA, and iCircDA-MF) are network-based approaches, and the rest are machine learning-based methods.…”

“…Lei and Bian [ 14 ] proposed an RWRKNN model, where the random walk algorithm with restart is used to weight the characteristics of circRNA and the disease, and KNN was used to make the final prediction. Wang et al [ 15 ] constructed a model named GCNCDA, which extracts features by using the graph convolutional neural network and predicts the potential circRNA-disease associations by forest penalizing attributes (Forest PA) classifier. Wang et al [ 16 ] used a deep generative adversarial network to draw features from multi-source fusion information.…”

Circular RNAs: Their Role in the Pathogenesis and Orchestration of Breast Cancer

“…For instance, hsa_circ_0001875 was upregulated and hsa_circ_0006054 was significantly downregulated in BC tissues (Li M. et al, 2020 ). Coincidentally, Wang et al ( 2020 ) also proposed a computational method called GCNCDA. In the case study experiments on diseases including BC, glioma, and colorectal cancer, about 16, 15, and 17 of the top 20 candidate circRNAs, respectively, with the highest prediction scores were verified by relevant literature and databases, suggesting that GCNCDA was effective in predicting potential circRNA-disease associations.…”

GATCDA: Predicting circRNA-Disease Associations Based on Graph Attention Network

“…For example, Wang's method [27] applies a convolutional neural network to discover unknown circRNAdisease associations. In 2020, GCNCDA was proposed based on a graph convolutional network [28].…”