NCPCDA: network consistency projection for circRNA–disease association prediction

Li, Guanghui; Yue, Yingjie; Liang, Cheng; Xiao, Qiu; Ding, Pingjian; Luo, Jiawei

doi:10.1039/c9ra06133a

Cited by 45 publications

(15 citation statements)

References 46 publications

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“…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.…”

Section: Resultsmentioning

confidence: 99%

“…To control the values of Ac and Ad within a specific range, we carried out the following processing for Ac and Ad [ 11 ]. …”

Section: Methodsmentioning

confidence: 99%

“…Yan et al [ 10 ] designed a DWNN-RLS model for predicting circRNA-disease associations by using the Kronecker product kernel, which is based on the regularized least-squares method. Li et al [ 11 ] utilized the NCPCDA method to identify potential circRNA-disease associations using multi-view similarity data, including circRNA functional similarity, disease semantic similarity, and association profile similarity. Lei and Fang [ 12 ] proposed a gradient boosting decision tree algorithm to make the final prediction using multiple biological data on circRNAs and diseases.…”

Section: Introductionmentioning

confidence: 99%

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Double matrix completion for circRNA-disease association prediction

et al. 2021

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Background Circular 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. Results In 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. Conclusion The results show that our method works well for recommending the potential circRNAs for a disease for biological experiments.

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Section: Resultsmentioning

confidence: 99%

“…To control the values of Ac and Ad within a specific range, we carried out the following processing for Ac and Ad [ 11 ]. …”

Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Double matrix completion for circRNA-disease association prediction

et al. 2021

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“…(1) Network-based circRNA-disease association prediction method: Fan et al [28] used known circRNA-disease associations, circRNA expression profile similarities, and disease phenotype similarities to construct the circRNA-disease heterogeneous network and then used KATZ to predict potential associations between circRNAs and diseases. Li et al [29] integrated known circRNA-disease associations, circRNA functional similarities, and disease semantic similarities and utilized network-consistent projections to identify potential circRNA-disease associations. Zhao et al [30] developed an ensemble learning algorithm to predict the potential association between circRNA and diseases.…”

Section: Related Workmentioning

confidence: 99%

Prediction of circRNA‐miRNA Associations Based on Network Embedding

et al. 2021

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circRNA is a novel class of noncoding RNA with closed-loop structure. Increasing biological experiments have shown that circRNAs play an important role in many diseases by acting as a miRNA sponge to indirectly regulate the expression of miRNA target genes. Therefore, predicting associations between circRNAs and miRNAs can promote the understanding of pathogenesis of disease. In this paper, we propose a new computational method, NECMA, based on network embedding to predict potential associations between circRNAs and miRNAs. In our method, the Gaussian interaction profile (GIP) kernel similarities of circRNA and miRNA are calculated based on the known circRNA-miRNA associations, respectively. Then, the circRNA-miRNA association network, circRNA GIP kernel similarity network, and miRNA GIP kernel similarity network are utilized to construct the heterogeneous network. Furthermore, the network embedding algorithm is used to extract potential features of circRNA and miRNA from the heterogeneous network, respectively. Finally, the associations between circRNAs and miRNAs are predicted by using neighborhood regularization logic matrix decomposition and inner product. The performance of NECMA is evaluated by using ten-fold cross-validation. The results show that this method has better prediction accuracy than other state-of-the-art methods.

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“…Based on the changing threshold, we can calculate different FPRs and TPRs, which are used to draw the ROC curve and calculate the corresponding Area Under the Curve (AUC) value. In order to validate the performance of PCD--MVMF, other prediction methods, KATZ [56], BiRW_avg [57], SIMCCDA [58], MRLDC [36], and NCPCDA [59] are compared with PCD_MVMF, as shown in Fig. 2.…”

Section: Cross-validationmentioning

confidence: 99%

CircRNA-disease associations prediction based on metapath2vec++ and matrix factorization

Zhang

Lei

Fang

et al. 2020

Big Data Min. Anal.

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Circular RNA (circRNA) is a novel non-coding endogenous RNAs. Evidence has shown that circRNAs are related to many biological processes and play essential roles in different biological functions. Although increasing numbers of circRNAs are discovered using high-throughput sequencing technologies, these techniques are still time-consuming and costly. In this study, we propose a computational method to predict circRNA-disesae associations which is based on metapath2vec++ and matrix factorization with integrated multiple data (called PCD_MVMF). To construct more reliable networks, various aspects are considered. Firstly, circRNA annotation, sequence, and functional similarity networks are established, and disease-related genes and semantics are adopted to construct disease functional and semantic similarity networks. Secondly, metapath2vec++ is applied on an integrated heterogeneous network to learn the embedded features and initial prediction score. Finally, we use matrix factorization, take similarity as a constraint, and optimize it to obtain the final prediction results. Leave-one-out cross-validation, five-fold cross-validation, and f-measure are adopted to evaluate the performance of PCD_MVMF. These evaluation metrics verify that PCD_MVM F has better prediction performance than other methods. To further illustrate the performance of PCD_MVMF, case studies of common diseases are conducted. Therefore, PCD_MVMF can be regarded as a reliable and useful circRNA-disease association prediction tool.

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NCPCDA: network consistency projection for circRNA–disease association prediction

Cited by 45 publications

References 46 publications

Double matrix completion for circRNA-disease association prediction

Double matrix completion for circRNA-disease association prediction

Prediction of circRNA‐miRNA Associations Based on Network Embedding

CircRNA-disease associations prediction based on metapath2vec++ and matrix factorization

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