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HMCDA: a novel method based on the heterogeneous graph neural network and metapath for circRNA-disease associations prediction
Circular RNA (CircRNA) is a type of non-coding RNAs in which both ends are covalently linked. Researchers have demonstrated that many circRNAs can...
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MSPCD: predicting circRNA-disease associations via integrating multi-source data and hierarchical neural network
BackgroundIncreasing evidence shows that circRNA plays an essential regulatory role in diseases through interactions with disease-related miRNAs....
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DCDA: CircRNA–Disease Association Prediction with Feed-Forward Neural Network and Deep Autoencoder
Circular RNA is a single-stranded RNA with a closed-loop structure. In recent years, academic research has revealed that circular RNAs play critical...
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CircWalk: a novel approach to predict CircRNA-disease association based on heterogeneous network representation learning
BackgroundSeveral types of RNA in the cell are usually involved in biological processes with multiple functions. Coding RNAs code for proteins while...
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Predicting circRNA-Disease Associations Based on Deep Matrix Factorization with Multi-source Fusion
Recently, circRNAs with covalently closed loops have been discovered to play important parts in the progression of diseases. Nevertheless, the study...
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CRPGCN: predicting circRNA-disease associations using graph convolutional network based on heterogeneous network
BackgroundThe existing studies show that circRNAs can be used as a biomarker of diseases and play a prominent role in the treatment and diagnosis of...
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Double matrix completion for circRNA-disease association prediction
BackgroundCircular RNAs (circRNAs) are a class of single-stranded RNA molecules with a closed-loop structure. A growing body of research has shown...
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iCDA-CMG: identifying circRNA-disease associations by federating multi-similarity fusion and collective matrix completion
Circular RNAs (circRNAs) are a special class of non-coding RNAs with covalently closed-loop structures. Studies prove that circRNAs perform critical...
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GDCL-NcDA: identifying non-coding RNA-disease associations via contrastive learning between deep graph learning and deep matrix factorization
Non-coding RNAs (ncRNAs) draw much attention from studies widely in recent years because they play vital roles in life activities. As a good...
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Inferring circRNA-drug sensitivity associations via dual hierarchical attention networks and multiple kernel fusion
Increasing evidence has shown that the expression of circular RNAs (circRNAs) can affect the drug sensitivity of cells and significantly influence...
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A computational model of circRNA-associated diseases based on a graph neural network: prediction and case studies for follow-up experimental validation
BackgroundCircular RNAs (circRNAs) have been confirmed to play a vital role in the occurrence and development of diseases. Exploring the relationship...
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DAE-CFR: detecting microRNA-disease associations using deep autoencoder and combined feature representation
BackgroundMicroRNA (miRNA) has been shown to play a key role in the occurrence and progression of diseases, making uncovering miRNA-disease...
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Predicting circRNA-drug sensitivity associations via graph attention auto-encoder
BackgroundCircular RNAs (circRNAs) play essential roles in cancer development and therapy resistance. Many studies have shown that circRNA is closely...
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DWNN-RLS: regularized least squares method for predicting circRNA-disease associations
BackgroundMany evidences have demonstrated that circRNAs (circular RNA) play important roles in controlling gene expression of human, mouse and...
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MSCFS: inferring circRNA functional similarity based on multiple data sources
BackgroundMore and more evidence shows that circRNA plays an important role in various biological processes and human health. Therefore, inferring...
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DNRLCNN: A CNN Framework for Identifying MiRNA–Disease Associations Using Latent Feature Matrix Extraction with Positive Samples
Emerging evidence indicates that miRNAs have strong relationships with many human diseases. Investigating the associations will contribute to...
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Long non-coding RNA and circular RNA: new perspectives for molecular pathophysiology of atrial fibrillation
Many studies have demonstrated the association of atrial fibrillation (AF) with endogenous genetic regulatory mechanisms. These interactions could...
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Prediction of Back-splicing sites for CircRNA formation based on convolutional neural networks
BackgroundCircular RNAs (CircRNAs) play critical roles in gene expression regulation and disease development. Understanding the regulation mechanism...
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Non-coding RNAs in human health and disease: potential function as biomarkers and therapeutic targets
Human diseases have been a critical threat from the beginning of human history. Knowing the origin, course of action and treatment of any disease...
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Identification of circular RNA BTBD7_hsa_circ_0000563 as a novel biomarker for coronary artery disease and the functional discovery of BTBD7_hsa_circ_0000563 based on peripheral blood mononuclear cells: a case control study
BackgroundBTBD7_hsa_circ_0000563 is a novel circRNA and contains conserved binding sites with RNA-binding proteins. However, BTBD7_hsa_circ_0000563...
