CircRNA-Disease Association Prediction based on Heterogeneous Graph Representation

Circular RNAs (circRNAs) are covalently closed single-stranded RNA molecules, which have many biological functions. Previous experiments have shown that circRNAs are involved in numerous biological processes, especially regulatory functions. It has also been found that circRNAs are associated with complex diseases of human beings. Therefore, predicting the associations of circRNA with disease (called circRNA-disease associations) is useful for disease prevention, diagnosis and treatment. In this work, we propose a novel computational approach called GGCDA based on the Graph Attention Network (GAT) and Graph Convolutional Network (GCN) to predict circRNA-disease associations. Firstly, GGCDA combines circRNA sequence similarity, disease semantic similarity and corresponding Gaussian interaction profile kernel similarity, and then a random walk with restart algorithm (RWR) is used to obtain the preliminary features of circRNA and disease. Secondly, a heterogeneous graph is constructed from the known circRNA-disease association network and the calculated similarity of circRNAs and diseases. Thirdly, the multi-head Graph Attention Network (GAT) is adopted to obtain different weights of circRNA and disease features, and then GCN is employed to aggregate the features of adjacent nodes in the network and the features of the nodes themselves, so as to obtain multi-view circRNA and disease features. Finally, we combined a multi-layer fully connected neural network to predict the associations of circRNAs with diseases. In comparison with state-of-the-art methods, GGCDA can achieve AUC values of 0.9625 and 0.9485 under the results of fivefold cross-validation on two datasets, and AUC of 0.8227 on the independent test set. Case studies further demonstrate that our approach is promising for discovering potential circRNA-disease associations.

Exploring the relationship between circular RNA (circRNA) and disease is beneficial for revealing the mechanisms of disease pathogenesis. However, a blind search for all possible associations between circRNAs and diseases through biological experiments is time-consuming. Although some prediction methods have been proposed, they still have limitations. In this study, a novel computational framework, called GATCL2CD, is proposed to forecast unknown circRNA-disease associations (CDAs). First, we calculate Gaussian interactive profile kernel (GIP) similarity and semantic similarity for diseases, circRNA sequence similarity and function similarity, and GIPs for circRNAs. Then, we combine them to construct a heterogeneous graph. Thereafter, GATCL2CD proposes a feature convolution learning framework, that uses a multi-head dynamic attention mechanism to obtain different aggregated representations of features that correspond to the nodes in the heterogeneous graph. Then, it extracts rich higher-order features from the stacked feature representations of each node by using of a single-layer convolutional neural network with filter kernels of different sizes. Finally, a pairwise element-wise product operation is implemented to capture the interactions of higher-order feature representations, and a multilayer perceptron neural network is introduced as an efficient classifier for inferring potential CDAs. Major experimental results under 5-fold cross-validation (5-fold CV) on three different datasets show that GATCL2CD is superior to five other state-of-the-art methods. Furthermore, case studies demonstrate the suitability of GATCL2CD as a useful tool for identifying potential disease-related circRNAs.

Circular RNA (circRNA) is a closed-loop structural non-coding RNA molecule which plays a significant role during the gene regulation processes. There are many previous studies shown that circRNAs can be regarded as the sponges of miRNAs. Thus, circRNA is also a key point for disease diagnosing, treating and inferring. However, traditional experimental approaches to verify the associations between the circRNA and disease are time-consuming and money-consuming. There are few computational models to predict potential circRNA-disease associations, which become our motivation to propose a new computational model. In this study, we propose a machine learning based computational model named Gradient Boosting Decision Tree with multiple biological data to predict circRNA-disease associations (GBDTCDA). The known circRNA-disease associations' data are downloaded from cricR2Disease database (http://bioinfo.snnu.edu.cn/CircR2Disease/). The feature vector of each circRNA-disease association pair is composed of four parts, which are the statistics information of different biological networks, the graph theory information of different biological networks, circRNA-disease associations' network information and circRNA nucleotide sequence information, respectively. Therefore, we use those feature vectors to train the gradient boosting decision tree regression model. Then, the leave one out cross validation (LOOCV) is adopted to evaluate the performance of our computational model. As for predicting some common diseases related circRNAs, our method GBDTCDA also obtains the better results. The Area under the ROC Curve (AUC) values of Basal cell carcinoma, Non-small cell lung cancer and cervical cancer are 95.8%, 88.3% and 93.5%, respectively. For further illustrating the performance of GBDTCDA, a case study of breast cancer is also supplemented in this study. Thus, our proposed method GBDTCDA is a powerful tool to predict potential circRNA-disease associations based on experimental results and analyses.

Circular RNA (circRNA) is a kind of novel discovered non-coding RNA molecule with a closed loop structure, which plays a critical regulatory role in human diseases. Identifying the association between circRNAs and diseases has important potential value for the diagnosis and treatment of complex human diseases. Although biological experiments can more accurately identify the association between circRNAs and diseases, they are usually blind and limited by small scale and high cost. Therefore, there is an urgent need for efficient and feasible computational methods to predict the potential circRNA-disease associations on a large scale, so as to provide the most promising candidate for biological experiments. In this paper, we propose a novel computational method based on the deep Generative Adversarial Network (GAN) algorithm combined with the multi-source similarity information to predict the circRNA-disease associations. Firstly, we fuse the multi-source information of disease semantic similarity, disease and circRNA Gaussian interaction profile kernel similarity, and then use GAN to extract the hidden features of fusion information objectively and effectively in the way of confrontation learning, and finally send them to Logistic Model Tree (LMT) classifier for accurate prediction. The 5-fold cross-validation experiment of the proposed model achieved 89.2% accuracy with 89.4% precision at the AUC of 90.6% on the CIRCR2Disease dataset. Compared with the state-of-the-art SVM classifier and other feature extraction methods, the proposed model shows strong competitiveness. In addition, the predicted results of this model are supported by the biological experiments, and 9 of the top 15 circRNA-disease associations with the highest scores were confirmed by recently published literature. These promising results indicate that the proposed model is an effective tool for predicting circRNA-disease associations and can provide reliable candidates for biological experiments.

A growing body of evidence indicates that circular RNAs (circRNAs) play a pivotal role in various biological processes and have a close association with the initiation and progression of diseases. Moreover, circRNAs are considered as promising biomarkers for disease diagnosis owing to their characteristics of conservation, stability and universality. Inferring disease–circRNA relationships will contribute to the understanding of disease pathology. However, it is costly and laborious to discover novel disease–circRNA interactions by wet-lab experiments, and few computational methods have been devoted to predicting potential circRNAs for diseases. Here, we advance a computational method (NCPCDA) to identify novel circRNA–disease associations based on network consistency projection. For starters, we make use of multi-view similarity data, including circRNA functional similarity, disease semantic similarity, and association profile similarity, to construct the integrated circRNA similarity and disease similarity. Then, we project circRNA space and disease space on the circRNA–disease interaction network, respectively. Finally, we can obtain the predicted circRNA–disease association score matrix by combining the above two space projection scores. Simulation results show that NCPCDA can efficiently infer disease–circRNA relationships with high accuracy, obtaining AUCs of 0.9541 and 0.9201 in leave-one-out cross validation and five-fold cross validation, respectively. Furthermore, case studies also suggest that NCPCDA is promising for discovering new disease–circRNA interactions. The NCPCDA dataset and code, as well as the detailed readme file for our code, can be downloaded from Github (https://github.com/ghli16/NNCPCD).

Increasing evidence suggest that circRNA, as one of the most promising emerging biomarkers, has a very close relationship with diseases. Exploring the relationship between circRNA and diseases can provide novel perspective for diseases diagnosis and pathogenesis. The existing circRNA-disease association (CDA) prediction models, however, generally treat the data attributes equally, do not pay special attention to the attributes with more significant influence, and do not make full use of the correlation and symbiosis between attributes to dig into the latent semantic information of the data. Therefore, in response to the above problems, this paper proposes a natural semantic enhancement method NSECDA to predict CDA. In practical terms, we first recognize the circRNA sequence as a biological language, and analyze its natural semantic properties through the natural language understanding theory; then integrate it with disease attributes, circRNA and disease Gaussian Interaction Profile (GIP) kernel attributes, and use Graph Attention Network (GAT) to focus on the influential attributes, so as to mine the deeply hidden features; finally, the Rotation Forest (RoF) classifier was used to accurately determine CDA. In the gold standard data set CircR2Disease, NSECDA achieved 92.49% accuracy with 0.9225 AUC score. In comparison with the non-natural semantic enhancement model and other classifier models, NSECDA also shows competitive performance. Additionally, 25 of the CDA pairs with unknown associations in the top 30 prediction scores of NSECDA have been proven by newly reported studies. These achievements suggest that NSECDA is an effective model to predict CDA, which can provide credible candidate for subsequent wet experiments, thus significantly reducing the scope of investigations.

Circular RNAs (circRNAs) are a category of noncoding RNAs that exist in great numbers in eukaryotes. They have recently been discovered to be crucial in the growth of tumors. Therefore, it is important to explore the association of circRNAs with disease. This paper proposes a new method based on DeepWalk and nonnegative matrix factorization (DWNMF) to predict circRNA-disease association. Based on the known circRNA-disease association, we calculate the topological similarity of circRNA and disease via the DeepWalk-based method to learn the node features on the association network. Next, the functional similarity of the circRNAs and the semantic similarity of the diseases are fused with their respective topological similarities at different scales. Then, we use the improved weighted K-nearest neighbor (IWKNN) method to preprocess the circRNA-disease association network and correct nonnegative associations by setting different parameters K1 and K2 in the circRNA and disease matrices. Finally, the L2,1-norm, dual-graph regularization term and Frobenius norm regularization term are introduced into the nonnegative matrix factorization model to predict the circRNA-disease correlation. We perform cross-validation on circR2Disease, circRNADisease, and MNDR. The numerical results show that DWNMF is an efficient tool for forecasting potential circRNA-disease relationships, outperforming other state-of-the-art approaches in terms of predictive performance.

Accumulating evidences have shown that circRNA plays an important role in human diseases. It can be used as potential biomarker for diagnose and treatment of disease. Although some computational methods have been proposed to predict circRNA-disease associations, the performance still need to be improved. In this paper, we propose a new computational model based on Improved Graph convolutional network and Negative Sampling to predict CircRNA-Disease Associations. In our method, it constructs the heterogeneous network based on known circRNA-disease associations. Then, an improved graph convolutional network is designed to obtain the feature vectors of circRNA and disease. Further, the multi-layer perceptron is employed to predict circRNA-disease associations based on the feature vectors of circRNA and disease. In addition, the negative sampling method is employed to reduce the effect of the noise samples, which selects negative samples based on circRNA's expression profile similarity and Gaussian Interaction Profile kernel similarity. The 5-fold cross validation is utilized to evaluate the performance of the method. The results show that IGNSCDA outperforms than other state-of-the-art methods in the prediction performance. Moreover, the case study shows that IGNSCDA is an effective tool for predicting potential circRNA-disease associations.

Accumulating evidences demonstrate that circular RNA (circRNA) plays an important role in human diseases. Identification of circRNA-disease associations can help for the diagnosis of human diseases, while the traditional method based on biological experiments is time-consuming. In order to address the limitation, a series of computational methods have been proposed in recent years. However, few works have summarized these methods or compared the performance of them. In this paper, we divided the existing methods into three categories: information propagation, traditional machine learning and deep learning. Then, the baseline methods in each category are introduced in detail. Further, 5 different datasets are collected, and 14 representative methods of each category are selected and compared in the 5-fold, 10-fold cross-validation and the de novo experiment. In order to further evaluate the effectiveness of these methods, six common cancers are selected to compare the number of correctly identified circRNA-disease associations in the top-10, top-20, top-50, top-100 and top-200. In addition, according to the results, the observation about the robustness and the character of these methods are concluded. Finally, the future directions and challenges are discussed.

GEHGAN[formula omitted] CircRNA–disease association prediction via graph embedding and heterogeneous graph attention network

BackgroundSeveral types of RNA in the cell are usually involved in biological processes with multiple functions. Coding RNAs code for proteins while non-coding RNAs regulate gene expression. Some single-strand RNAs can create a circular shape via the back splicing process and convert into a new type called circular RNA (circRNA). circRNAs are among the essential non-coding RNAs in the cell that involve multiple disorders. One of the critical functions of circRNAs is to regulate the expression of other genes through sponging micro RNAs (miRNAs) in diseases. This mechanism, known as the competing endogenous RNA (ceRNA) hypothesis, and additional information obtained from biological datasets can be used by computational approaches to predict novel associations between disease and circRNAs.ResultsWe applied multiple classifiers to validate the extracted features from the heterogeneous network and selected the most appropriate one based on some evaluation criteria. Then, the XGBoost is utilized in our pipeline to generate a novel approach, called CircWalk, to predict CircRNA-Disease associations. Our results demonstrate that CircWalk has reasonable accuracy and AUC compared with other state-of-the-art algorithms. We also use CircWalk to predict novel circRNAs associated with lung, gastric, and colorectal cancers as a case study. The results show that our approach can accurately detect novel circRNAs related to these diseases.ConclusionsConsidering the ceRNA hypothesis, we integrate multiple resources to construct a heterogeneous network from circRNAs, mRNAs, miRNAs, and diseases. Next, the DeepWalk algorithm is applied to the network to extract feature vectors for circRNAs and diseases. The extracted features are used to learn a classifier and generate a model to predict novel CircRNA-Disease associations. Our approach uses the concept of the ceRNA hypothesis and the miRNA sponge effect of circRNAs to predict their associations with diseases. Our results show that this outlook could help identify CircRNA-Disease associations more accurately.

Recent studies show that circRNAs have critical roles in many biological processes. Knowing the associations between circRNAs and diseases may contribute to the understanding of the mechanism of circRNAs and to the diagnostic and therapeutic methods of diseases at the molecular level. A small number of computation models have been developed to estimate CircRNA-disease associations. Therefore, in this study, a computational model has been developed. Similarity matrices have been obtained for circRNA and disease respectively by applying gaussian on the data obtained from the circRNADisease database. Then, random walk with restart algorithm applied on the combined matrices. The AUC value was obtained by 5-fold cross validation is 0.861 and this demonstrates the reliability of the model.

CircularRNA (circRNA) is a class of noncoding RNA with high conservation and stability, which is considered as an important disease biomarker and drug target. Accumulating pieces of evidence have indicated that circRNA plays a crucial role in the pathogenesis and progression of many complex diseases. As the biological experiments are time-consuming and labor-intensive, developing an accurate computational prediction method has become indispensable to identify disease-related circRNAs. We presented a hybrid graph representation learning framework, named GraphCDA, for predicting the potential circRNA-disease associations. Firstly, the circRNA-circRNA similarity network and disease-disease similarity network were constructed to characterize the relationships of circRNAs and diseases, respectively. Secondly, a hybrid graph embedding model combining Graph Convolutional Networks and Graph Attention Networks was introduced to learn the feature representations of circRNAs and diseases simultaneously. Finally, the learned representations were concatenated and employed to build the prediction model for identifying the circRNA-disease associations. A series of experimental results demonstrated that GraphCDA outperformed other state-of-the-art methods on several public databases. Moreover, GraphCDA could achieve good performance when only using a small number of known circRNA-disease associations as the training set. Besides, case studies conducted on several human diseases further confirmed the prediction capability of GraphCDA for predicting potential disease-related circRNAs. In conclusion, extensive experimental results indicated that GraphCDA could serve as a reliable tool for exploring the regulatory role of circRNAs in complex diseases.

CircRNA is closely related to human disease, so it is important to predict circRNA-disease association (CDA). However, the traditional biological detection methods have high difficulty and low accuracy, and computational methods represented by deep learning ignore the ability of the model to explicitly extract local depth information of the CDA. We propose a model based on knowledge graph from recursion and attention aggregation for circRNA-disease association prediction (KGRACDA). This model combines explicit structural features and implicit embedding information of graphs, optimizing graph embedding vectors. First, we built large-scale, multi-source heterogeneous datasets and construct a knowledge graph of multiple RNAs and diseases. After that, we use a recursive method to build multi-hop subgraphs and optimize graph attention mechanism by gating mechanism, mining local depth information. At the same time, the model uses multi-head attention mechanism to balance global and local depth features of graphs, and generate CDA prediction scores. KGRACDA surpasses other methods by capturing local and global depth information related to CDA. We update an interactive web platform HNRBase v2.0, which visualizes circRNA data, and allows users to download data and predict CDA using model.

BackgroundCircular RNAs (circRNAs) are a class of single-stranded RNA molecules with a closed-loop structure. A growing body of research has shown that circRNAs are closely related to the development of diseases. Because biological experiments to verify circRNA-disease associations are time-consuming and wasteful of resources, it is necessary to propose a reliable computational method to predict the potential candidate circRNA-disease associations for biological experiments to make them more efficient.ResultsIn this paper, we propose a double matrix completion method (DMCCDA) for predicting potential circRNA-disease associations. First, we constructed a similarity matrix of circRNA and disease according to circRNA sequence information and semantic disease information. We also built a Gauss interaction profile similarity matrix for circRNA and disease based on experimentally verified circRNA-disease associations. Then, the corresponding circRNA sequence similarity and semantic similarity of disease are used to update the association matrix from the perspective of circRNA and disease, respectively, by matrix multiplication. Finally, from the perspective of circRNA and disease, matrix completion is used to update the matrix block, which is formed by splicing the association matrix obtained in the previous step with the corresponding Gaussian similarity matrix. Compared with other approaches, the model of DMCCDA has a relatively good result in leave-one-out cross-validation and five-fold cross-validation. Additionally, the results of the case studies illustrate the effectiveness of the DMCCDA model.ConclusionThe results show that our method works well for recommending the potential circRNAs for a disease for biological experiments.