AI Medical Compendium Topic

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

RNA, Messenger

Showing 151 to 160 of 221 articles

Clear Filters

An improved poly(A) motifs recognition method based on decision level fusion.

Computational biology and chemistry
Polyadenylation is the process of addition of poly(A) tail to mRNA 3' ends. Identification of motifs controlling polyadenylation plays an essential role in improving genome annotation accuracy and better understanding of the mechanisms governing gene...

Molecular classification of amyotrophic lateral sclerosis by unsupervised clustering of gene expression in motor cortex.

Neurobiology of disease
Amyotrophic lateral sclerosis (ALS) is a rapidly progressive and ultimately fatal neurodegenerative disease, caused by the loss of motor neurons in the brain and spinal cord. Although 10% of ALS cases are familial (FALS), the majority are sporadic (S...

Analysis of RNA translation with a deep learning architecture provides new insight into translation control.

Nucleic acids research
Accurate annotation of coding regions in RNAs is essential for understanding gene translation. We developed a deep neural network to directly predict and analyze translation initiation and termination sites from RNA sequences. Trained with human tran...

AI techniques have facilitated the understanding of epitranscriptome distribution.

Cell genomics
N-methyladenosine (m6A), the most prevalent internal mRNA modification in higher eukaryotes, plays diverse roles in cellular regulation. By incorporating both sequence- and genome-derived features, Fan et al. designed a novel Transformer-BiGRU framew...

miCGR: interpretable deep neural network for predicting both site-level and gene-level functional targets of microRNA.

Briefings in bioinformatics
MicroRNAs (miRNAs) are critical regulators in various biological processes to cleave or repress translation of messenger RNAs (mRNAs). Accurately predicting miRNA targets is essential for developing miRNA-based therapies for diseases such as cancer a...

A deep learning method to integrate extracelluar miRNA with mRNA for cancer studies.

Bioinformatics (Oxford, England)
MOTIVATION: Extracellular miRNAs (exmiRs) and intracellular mRNAs both can serve as promising biomarkers and therapeutic targets for various diseases. However, exmiR expression data is often noisy, and obtaining intracellular mRNA expression data usu...

DPNN-ac4C: a dual-path neural network with self-attention mechanism for identification of N4-acetylcytidine (ac4C) in mRNA.

Bioinformatics (Oxford, England)
MOTIVATION: The modification of N4-acetylcytidine (ac4C) in RNA is a conserved epigenetic mark that plays a crucial role in post-transcriptional regulation, mRNA stability, and translation efficiency. Traditional methods for detecting ac4C modificati...

siRNADiscovery: a graph neural network for siRNA efficacy prediction via deep RNA sequence analysis.

Briefings in bioinformatics
The clinical adoption of small interfering RNAs (siRNAs) has prompted the development of various computational strategies for siRNA design, from traditional data analysis to advanced machine learning techniques. However, previous studies have inadequ...

m6ATM: a deep learning framework for demystifying the m6A epitranscriptome with Nanopore long-read RNA-seq data.

Briefings in bioinformatics
N6-methyladenosine (m6A) is one of the most abundant and well-known modifications in messenger RNAs since its discovery in the 1970s. Recent studies have demonstrated that m6A is involved in various biological processes, such as alternative splicing ...

MSlocPRED: deep transfer learning-based identification of multi-label mRNA subcellular localization.

Briefings in bioinformatics
Subcellular localization of messenger ribonucleic acid (mRNA) is a universal mechanism for precise and efficient control of the translation process. Although many computational methods have been constructed by researchers for predicting mRNA subcellu...