AIMC Topic: RNA, Circular

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MNMDCDA: prediction of circRNA-disease associations by learning mixed neighborhood information from multiple distances.

Briefings in bioinformatics
Emerging evidence suggests that circular RNA (circRNA) is an important regulator of a variety of pathological processes and serves as a promising biomarker for many complex human diseases. Nevertheless, there are relatively few known circRNA-disease ...

Deep learning models for disease-associated circRNA prediction: a review.

Briefings in bioinformatics
Emerging evidence indicates that circular RNAs (circRNAs) can provide new insights and potential therapeutic targets for disease diagnosis and treatment. However, traditional biological experiments are expensive and time-consuming. Recently, deep lea...

A machine learning framework based on multi-source feature fusion for circRNA-disease association prediction.

Briefings in bioinformatics
Circular RNAs (circRNAs) are involved in the regulatory mechanisms of multiple complex diseases, and the identification of their associations is critical to the diagnosis and treatment of diseases. In recent years, many computational methods have bee...

GMNN2CD: identification of circRNA-disease associations based on variational inference and graph Markov neural networks.

Bioinformatics (Oxford, England)
MOTIVATION: With the analysis of the characteristic and function of circular RNAs (circRNAs), people have realized that they play a critical role in the diseases. Exploring the relationship between circRNAs and diseases is of far-reaching significanc...

circRNA-binding protein site prediction based on multi-view deep learning, subspace learning and multi-view classifier.

Briefings in bioinformatics
Circular RNAs (circRNAs) generally bind to RNA-binding proteins (RBPs) to play an important role in the regulation of autoimmune diseases. Thus, it is crucial to study the binding sites of RBPs on circRNAs. Although many methods, including traditiona...

Prediction of RBP binding sites on circRNAs using an LSTM-based deep sequence learning architecture.

Briefings in bioinformatics
Circular RNAs (circRNAs) are widely expressed in highly diverged eukaryotes. Although circRNAs have been known for many years, their function remains unclear. Interaction with RNA-binding protein (RBP) to influence post-transcriptional regulation is ...

SGANRDA: semi-supervised generative adversarial networks for predicting circRNA-disease associations.

Briefings in bioinformatics
Emerging research shows that circular RNA (circRNA) plays a crucial role in the diagnosis, occurrence and prognosis of complex human diseases. Compared with traditional biological experiments, the computational method of fusing multi-source biologica...

Feature extraction approaches for biological sequences: a comparative study of mathematical features.

Briefings in bioinformatics
As consequence of the various genomic sequencing projects, an increasing volume of biological sequence data is being produced. Although machine learning algorithms have been successfully applied to a large number of genomic sequence-related problems,...

A comprehensive survey on computational methods of non-coding RNA and disease association prediction.

Briefings in bioinformatics
The studies on relationships between non-coding RNAs and diseases are widely carried out in recent years. A large number of experimental methods and technologies of producing biological data have also been developed. However, due to their high labor ...

An efficient approach based on multi-sources information to predict circRNA-disease associations using deep convolutional neural network.

Bioinformatics (Oxford, England)
MOTIVATION: Emerging evidence indicates that circular RNA (circRNA) plays a crucial role in human disease. Using circRNA as biomarker gives rise to a new perspective regarding our diagnosing of diseases and understanding of disease pathogenesis. Howe...