AIMC Topic: RNA

Clear Filters Showing 281 to 290 of 355 articles

CheRRI-Accurate classification of the biological relevance of putative RNA-RNA interaction sites.

GigaScience
BACKGROUND: RNA-RNA interactions are key to a wide range of cellular functions. The detection of potential interactions helps to understand the underlying processes. However, potential interactions identified via in silico or experimental high-throug...

H2Opred: a robust and efficient hybrid deep learning model for predicting 2'-O-methylation sites in human RNA.

Briefings in bioinformatics
2'-O-methylation (2OM) is the most common post-transcriptional modification of RNA. It plays a crucial role in RNA splicing, RNA stability and innate immunity. Despite advances in high-throughput detection, the chemical stability of 2OM makes it diff...

Accurately identifying nucleic-acid-binding sites through geometric graph learning on language model predicted structures.

Briefings in bioinformatics
The interactions between nucleic acids and proteins are important in diverse biological processes. The high-quality prediction of nucleic-acid-binding sites continues to pose a significant challenge. Presently, the predictive efficacy of sequence-bas...

The Deep Generative Decoder: MAP estimation of representations improves modelling of single-cell RNA data.

Bioinformatics (Oxford, England)
MOTIVATION: Learning low-dimensional representations of single-cell transcriptomics has become instrumental to its downstream analysis. The state of the art is currently represented by neural network models, such as variational autoencoders, which us...

Recent trends in RNA informatics: a review of machine learning and deep learning for RNA secondary structure prediction and RNA drug discovery.

Briefings in bioinformatics
Computational analysis of RNA sequences constitutes a crucial step in the field of RNA biology. As in other domains of the life sciences, the incorporation of artificial intelligence and machine learning techniques into RNA sequence analysis has gain...

Structural interaction fingerprints and machine learning for predicting and explaining binding of small molecule ligands to RNA.

Briefings in bioinformatics
Ribonucleic acids (RNAs) play crucial roles in living organisms and some of them, such as bacterial ribosomes and precursor messenger RNA, are targets of small molecule drugs, whereas others, e.g. bacterial riboswitches or viral RNA motifs are consid...

Concepts and methods for transcriptome-wide prediction of chemical messenger RNA modifications with machine learning.

Briefings in bioinformatics
The expanding field of epitranscriptomics might rival the epigenome in the diversity of biological processes impacted. In recent years, the development of new high-throughput experimental and computational techniques has been a key driving force in d...

Machine learning for RNA 2D structure prediction benchmarked on experimental data.

Briefings in bioinformatics
Since the 1980s, dozens of computational methods have addressed the problem of predicting RNA secondary structure. Among them are those that follow standard optimization approaches and, more recently, machine learning (ML) algorithms. The former were...

Identification of metal ion-binding sites in RNA structures using deep learning method.

Briefings in bioinformatics
Metal ion is an indispensable factor for the proper folding, structural stability and functioning of RNA molecules. However, it is very difficult for experimental methods to detect them in RNAs. With the increase of experimentally resolved RNA struct...