AIMC Topic: RNA, Circular

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Predicting CircRNA-Disease Associations Based on Heterogeneous Graph Neural Network and Knowledge Graph Attribute Mining Attention.

Interdisciplinary sciences, computational life sciences
The exploration of associations between circular RNAs (circRNAs) and diseases contributes to a deeper understanding of the pathogenesis of diseases. Many computational methods have been proposed for circRNA-disease associations identification. Howeve...

CR-deal: Explainable Neural Network for circRNA-RBP Binding Site Recognition and Interpretation.

Interdisciplinary sciences, computational life sciences
circRNAs are a type of single-stranded non-coding RNA molecules, and their unique feature is their closed circular structure. The interaction between circRNAs and RNA-binding proteins (RBPs) plays a key role in biological functions and is crucial for...

Interpretable multi-instance heterogeneous graph network learning modelling CircRNA-drug sensitivity association prediction.

BMC biology
BACKGROUND: Different expression levels of circular RNAs (circRNAs) affect the sensitivity of human cells to drugs, thus producing different responses to the therapeutic effects of drugs. Using traditional biomedical experiments to discover and confi...

THGNCDA: circRNA-disease association prediction based on triple heterogeneous graph network.

Briefings in functional genomics
Circular RNAs (circRNAs) are a class of noncoding RNA molecules featuring a closed circular structure. They have been proved to play a significant role in the reduction of many diseases. Besides, many researches in clinical diagnosis and treatment of...

Identification of circRNA-disease associations via multi-model fusion and ensemble learning.

Journal of cellular and molecular medicine
Circular RNA (circRNA) is a common non-coding RNA and plays an important role in the diagnosis and therapy of human diseases, circRNA-disease associations prediction based on computational methods can provide a new way for better clinical diagnosis. ...

BioKA: a curated and integrated biomarker knowledgebase for animals.

Nucleic acids research
Biomarkers play an important role in various area such as personalized medicine, drug development, clinical care, and molecule breeding. However, existing animals' biomarker resources predominantly focus on human diseases, leaving a significant gap i...

MLNGCF: circRNA-disease associations prediction with multilayer attention neural graph-based collaborative filtering.

Bioinformatics (Oxford, England)
MOTIVATION: CircRNAs play a critical regulatory role in physiological processes, and the abnormal expression of circRNAs can mediate the processes of diseases. Therefore, exploring circRNAs-disease associations is gradually becoming an important area...

MPCLCDA: predicting circRNA-disease associations by using automatically selected meta-path and contrastive learning.

Briefings in bioinformatics
Circular RNA (circRNA) is closely associated with human diseases. Accordingly, identifying the associations between human diseases and circRNA can help in disease prevention, diagnosis and treatment. Traditional methods are time consuming and laborio...

Collaborative deep learning improves disease-related circRNA prediction based on multi-source functional information.

Briefings in bioinformatics
Emerging studies have shown that circular RNAs (circRNAs) are involved in a variety of biological processes and play a key role in disease diagnosing, treating and inferring. Although many methods, including traditional machine learning and deep lear...