AI Medical Compendium Topic

Explore the latest research on artificial intelligence and machine learning in medicine.

RNA

Showing 271 to 280 of 321 articles

Clear Filters

RNAI-FRID: novel feature representation method with information enhancement and dimension reduction for RNA-RNA interaction.

Briefings in bioinformatics
Different ribonucleic acids (RNAs) can interact to form regulatory networks that play important role in many life activities. Molecular biology experiments can confirm RNA-RNA interactions to facilitate the exploration of their biological functions, ...

PST-PRNA: prediction of RNA-binding sites using protein surface topography and deep learning.

Bioinformatics (Oxford, England)
MOTIVATION: Protein-RNA interactions play essential roles in many biological processes, including pre-mRNA processing, post-transcriptional gene regulation and RNA degradation. Accurate identification of binding sites on RNA-binding proteins (RBPs) i...

UFold: fast and accurate RNA secondary structure prediction with deep learning.

Nucleic acids research
For many RNA molecules, the secondary structure is essential for the correct function of the RNA. Predicting RNA secondary structure from nucleotide sequences is a long-standing problem in genomics, but the prediction performance has reached a platea...

Protein-RNA interaction prediction with deep learning: structure matters.

Briefings in bioinformatics
Protein-RNA interactions are of vital importance to a variety of cellular activities. Both experimental and computational techniques have been developed to study the interactions. Because of the limitation of the previous database, especially the lac...

DeepDISOBind: accurate prediction of RNA-, DNA- and protein-binding intrinsically disordered residues with deep multi-task learning.

Briefings in bioinformatics
Proteins with intrinsically disordered regions (IDRs) are common among eukaryotes. Many IDRs interact with nucleic acids and proteins. Annotation of these interactions is supported by computational predictors, but to date, only one tool that predicts...

NmRF: identification of multispecies RNA 2'-O-methylation modification sites from RNA sequences.

Briefings in bioinformatics
2'-O-methylation (Nm) is a post-transcriptional modification of RNA that is catalyzed by 2'-O-methyltransferase and involves replacing the H on the 2'-hydroxyl group with a methyl group. The 2'-O-methylation modification site is detected in a variety...

Deep learning-based advances and applications for single-cell RNA-sequencing data analysis.

Briefings in bioinformatics
The rapid development of single-cell RNA-sequencing (scRNA-seq) technology has raised significant computational and analytical challenges. The application of deep learning to scRNA-seq data analysis is rapidly evolving and can overcome the unique cha...

EDCNN: identification of genome-wide RNA-binding proteins using evolutionary deep convolutional neural network.

Bioinformatics (Oxford, England)
MOTIVATION: RNA-binding proteins (RBPs) are a group of proteins associated with RNA regulation and metabolism, and play an essential role in mediating the maturation, transport, localization and translation of RNA. Recently, Genome-wide RNA-binding e...

m1A-pred: Prediction of Modified 1-methyladenosine Sites in RNA Sequences through Artificial Intelligence.

Combinatorial chemistry & high throughput screening
BACKGROUND: The process of nucleotides modification or methyl groups addition to nucleotides is known as post-transcriptional modification (PTM). 1-methyladenosine (m1A) is a type of PTM formed by adding a methyl group to the nitrogen at the 1st posi...

Predicting RNA Secondary Structure Using In Vitro and In Vivo Data.

Methods in molecular biology (Clifton, N.J.)
The new flow of high-throughput RNA secondary structure data coming from different techniques allowed the further development of machine learning approaches. We developed CROSS and CROSSalive, two algorithms trained on experimental data able to predi...