AIMC Topic: RNA

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Interpretability-guided RNA N-methyladenosine modification site prediction with invertible neural networks.

Communications biology
As one of the most common and abundant post-transcriptional modifications, N-methyladenosine (mA) has been extensively studied for its essential regulatory role in gene expression and cell functions. The location of mA RNA modification sites, however...

Comprehensive datasets for RNA design, machine learning, and beyond.

Scientific reports
RNA molecules are essential in regulating biological processes such as gene expression, cellular differentiation, and development. Accurately predicting RNA secondary structures and designing sequences that fold into specific configurations remain si...

RiNALMo: general-purpose RNA language models can generalize well on structure prediction tasks.

Nature communications
While RNA has recently been recognized as an interesting small-molecule drug target, many challenges remain to be addressed before we take full advantage of it. This emphasizes the necessity to improve our understanding of its structures and function...

Deep generalizable prediction of RNA secondary structure via base pair motif energy.

Nature communications
Deep learning methods have demonstrated great performance for RNA secondary structure prediction. However, generalizability is a common unsolved issue on unseen out-of-distribution RNA families, which hinders further improvement of the accuracy and r...

Explainable RNA-Small Molecule Binding Affinity Prediction Based on Multiview Enhancement Learning.

Journal of chemical information and modeling
RNA has the potential to serve as a drug target, requiring RNA-small molecule binding affinity to screen potential drugs generally. However, accurately predicting RNA-small molecule binding affinity remains a highly challenging task. This study propo...

Uncertainty Quantification and Temperature Scaling Calibration for Protein-RNA Binding Site Prediction.

Journal of chemical information and modeling
The black-box nature of deep learning has increasingly drawn attention to the reliability and uncertainty of predictive models. Currently, several uncertainty quantification (UQ) methods have been proposed and successfully applied in the fields of mo...

Generative and predictive neural networks for the design of functional RNA molecules.

Nature communications
RNA is a remarkably versatile molecule that has been engineered for applications in therapeutics, diagnostics, and in vivo information-processing systems. However, the complex relationship between the sequence, structure, and function of RNA often ne...

GINClus: RNA structural motif clustering using graph isomorphism network.

NAR genomics and bioinformatics
Ribonucleic acid (RNA) structural motif identification is a crucial step for understanding RNA structure and functionality. Due to the complexity and variations of RNA 3D structures, identifying RNA structural motifs is challenging and time-consuming...

A divide-and-conquer approach based on deep learning for long RNA secondary structure prediction: Focus on pseudoknots identification.

PloS one
The accurate prediction of RNA secondary structure, and pseudoknots in particular, is of great importance in understanding the functions of RNAs since they give insights into their folding in three-dimensional space. However, existing approaches ofte...

Artificial intelligence for RNA-ligand interaction prediction: advances and prospects.

Drug discovery today
Accurate prediction of RNA-ligand interactions is vital for understanding biological processes and advancing RNA-targeted drug discovery. Given their complexity, artificial intelligence (AI) is revolutionizing the study of RNA-ligand interactions, of...