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 read more 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 Custom Mug 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_MVMF 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.