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RNA

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Systematic benchmarking of deep-learning methods for tertiary RNA structure prediction.

PLoS computational biology
The 3D structure of RNA critically influences its functionality, and understanding this structure is vital for deciphering RNA biology. Experimental methods for determining RNA structures are labour-intensive, expensive, and time-consuming. Computati...

The prediction of RNA-small-molecule ligand binding affinity based on geometric deep learning.

Computational biology and chemistry
Small molecule-targeted RNA is an emerging technology that plays a pivotal role in drug discovery and inhibitor design, with widespread applications in disease treatment. Consequently, predicting RNA-small-molecule ligand interactions is crucial. Wit...

ZeRPI: A graph neural network model for zero-shot prediction of RNA-protein interactions.

Methods (San Diego, Calif.)
RNA-protein interactions are crucial for biological functions across multiple levels. RNA binding proteins (RBPs) intricately engage in diverse biological processes through specific RNA molecule interactions. Previous studies have revealed the indisp...

Enhanced Sampling Simulations of RNA-Peptide Binding Using Deep Learning Collective Variables.

Journal of chemical information and modeling
Enhanced sampling (ES) simulations of biomolecular recognition, such as binding small molecules to proteins and nucleic acid targets, protein-protein association, and protein-nucleic acid interactions, have gained significant attention in the simulat...

SequenceCraft: machine learning-based resource for exploratory analysis of RNA-cleaving deoxyribozymes.

BMC bioinformatics
BACKGROUND: Deoxyribozymes or DNAzymes represent artificial short DNA sequences bearing many catalytic properties. In particular, DNAzymes able to cleave RNA sequences have a huge potential in gene therapy and sequence-specific analytic detection of ...

RNA structure prediction using deep learning - A comprehensive review.

Computers in biology and medicine
In computational biology, accurate RNA structure prediction offers several benefits, including facilitating a better understanding of RNA functions and RNA-based drug design. Implementing deep learning techniques for RNA structure prediction has led ...

Identifying RNA-small Molecule Binding Sites Using Geometric Deep Learning with Language Models.

Journal of molecular biology
RNAs are emerging as promising therapeutic targets, yet identifying small molecules that bind to them remains a significant challenge in drug discovery. This underscores the crucial role of computational modeling in predicting RNA-small molecule bind...

RNA-protein interaction prediction using network-guided deep learning.

Communications biology
Accurate computational determination of RNA-protein interactions remains challenging, particularly when encountering unknown RNAs and proteins. The limited number of RNAs and their flexibility constrained the effectiveness of the deep-learning models...

Deep learning for RNA structure prediction.

Current opinion in structural biology
Predicting RNA structures from sequences with computational approaches is of vital importance in RNA biology considering the high costs of experimental determination. AI methods have revolutionized this field in recent years, enabling RNA structure p...

ERNIE-ac4C: A Novel Deep Learning Model for Effectively Predicting N4-acetylcytidine Sites.

Journal of molecular biology
RNA modifications are known to play a critical role in gene regulation and cellular processes. Specifically, N4-acetylcytidine (ac4C) modification has emerged as a significant marker involved in mRNA translation efficiency, stability, and various dis...