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 circRNA-disease associations. The FastGCN algorithm is then used to objectively extract the high-level 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, 10 of the top 15 circRNA-disease association candidates with the highest prediction scores were confirmed by recently published literature. These results suggest that GCNCDA can effectively predict potential circRNA-disease associations and provide highly credible candidates for biological experiments. Author SummaryThe recognition of circRNA-disease association is the key of disease diagnosis and treatment, and it is of great significance for exploring the pathogenesis of complex diseases. Computational methods can predicte the potential disease-related circRNAs quickly and accurately. Based on the hypothesis that circRNA with similar function tends to associate with similar disease, GCNCDA model is proposed to effectively predict the potential association between circRNAs and diseases by combining FastGCN algorithm. The performance of the model was verified by cross-validation experiments, different feature extraction algorithm and classifier models comparison experiments. Furthermore, 10 of the top 15 disease-associated circRNAs with the highest prediction scores were confirmed by recently published literature. It is anticipated that GCNCDA model can give priority to the most promising circRNA-disease associations on a large scale to provide reliable candidates for further biological experiment.

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 cir-cRNA-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.…”

NLPEI: A Novel Self-Interacting Protein Prediction Model Based on Natural Language Processing and Evolutionary Information

“…), negative predictive value (NPV ), and area under the receiver operating characteristic curve (AUC) to evaluate the model performance. 27,[29][30][31] These evaluation criteria can be described by formulas as follows:…”

“…), negative predictive value (NPV ), and area under the receiver operating characteristic curve (AUC) to evaluate the model performance. 27,[29][30][31] These evaluation criteria can be described by formulas as follows: Here TP represents the number of proteins with self-interactions that are correctly predicted, TN represents the number of proteins with self-interactions that are erroneously predicted, FP represents the number of proteins without selfinteractions that are correctly predicted, and FN represents the number of proteins without self-interactions that are erroneously predicted.…”

Graph Convolution Networks Using Message Passing and Multi-Source Similarity Features for Predicting circRNA-Disease Association

“…As a result, there is an increased attention on using deep graph learning to predict the RNAdisease associations as graph convolutional networks are proven to be an effective and efficient model for information propagation in networks. GCNCDA [29] is a work which utilizes graph convolution to predict circRNA-disease associations. They employ FastGCN to extract features of circRNA-disease associations and final predictions are based on another classifier Forest PA. Other works [30] and [31] use spectral based graph convolutions to predict lncRNA-disease and miRNA-disease associations respectively.…”