AIMC Topic: RNA, Circular

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Deep generative models design mRNA sequences with enhanced translational capacity and stability.

Science (New York, N.Y.)
Despite the success of messenger RNA (mRNA) COVID-19 vaccines, extending this modality to more diseases necessitates substantial enhancements. We present GEMORNA, a generative RNA model that uses transformer architectures tailored for mRNA coding seq...

A deep learning approach based on molecular graph features and residual blocks to predict interaction sites between CircRNA and RBP.

Biochemical and biophysical research communications
CircRNAs are ubiquitously expressed across diverse tissues and cells, playing a pivotal role not only in protein-mediated biological processes but also in disease prevention and therapeutics. RNA-RBP interactions are critical for deciphering gene reg...

DGCLCMI: a deep graph collaboration learning method to predict circRNA-miRNA interactions.

BMC biology
BACKGROUND: Numerous studies have shown that circRNA can act as a miRNA sponge, competitively binding to miRNAs, thereby regulating gene expression and disease progression. Due to the high cost and time-consuming nature of traditional wet lab experim...

Circular RNA discovery with emerging sequencing and deep learning technologies.

Nature genetics
Circular RNA (circRNA) represents a type of RNA molecule characterized by a closed-loop structure that is distinct from linear RNA counterparts. Recent studies have revealed the emerging role of these circular transcripts in gene regulation and disea...

RBPsuite 2.0: an updated RNA-protein binding site prediction suite with high coverage on species and proteins based on deep learning.

BMC biology
BACKGROUND: RNA-binding proteins (RBPs) play crucial roles in many biological processes, and computationally identifying RNA-RBP interactions provides insights into the biological mechanism of diseases associated with RBPs.

TransRM: Weakly supervised learning of translation-enhancing N6-methyladenosine (mA) in circular RNAs.

International journal of biological macromolecules
As our understanding of Circular RNAs (circRNAs) continues to expand, accumulating evidence has demonstrated that circRNAs can interact with microRNAs and RNA-binding proteins to modulate gene expression. More importantly, a subset of circRNAs has be...

MVGNCDA: Identifying Potential circRNA-Disease Associations Based on Multi-view Graph Convolutional Networks and Network Embeddings.

Interdisciplinary sciences, computational life sciences
Increasing evidences have indicated that circular RNAs play a crucial role in the onset and progression of various diseases. However, exploring potential disease-associated circRNAs using conventional experimental techniques remains both time-intensi...

metaCDA: A Novel Framework for CircRNA-Driven Drug Discovery Utilizing Adaptive Aggregation and Meta-Knowledge Learning.

Journal of chemical information and modeling
In the emerging field of RNA drugs, circular RNA (circRNA) has attracted much attention as a novel multifunctional therapeutic target. Delving deeper into the intricate interactions between circRNA and disease is critical for driving drug discovery e...

Circular RNA-Drug Association Prediction Based on Multi-Scale Convolutional Neural Networks and Adversarial Autoencoders.

International journal of molecular sciences
The prediction of circular RNA (circRNA)-drug associations plays a crucial role in understanding disease mechanisms and identifying potential therapeutic targets. Traditional methods often struggle to cope with the complexity of heterogeneous network...

CellCircLoc: Deep Neural Network for Predicting and Explaining Cell Line-Specific CircRNA Subcellular Localization.

IEEE journal of biomedical and health informatics
The subcellular localization of circular RNAs (circRNAs) is crucial for understanding their functional relevance and regulatory mechanisms. CircRNA subcellular localization exhibits variations across different cell lines, demonstrating the diversity ...