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Nucleotides

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RNAdegformer: accurate prediction of mRNA degradation at nucleotide resolution with deep learning.

Briefings in bioinformatics
Messenger RNA-based therapeutics have shown tremendous potential, as demonstrated by the rapid development of messenger RNA based vaccines for COVID-19. Nevertheless, distribution of mRNA vaccines worldwide has been hampered by mRNA's inherent therma...

Machine learning classifiers predict key genomic and evolutionary traits across the kingdoms of life.

Scientific reports
In this study, we investigate how an organism's codon usage bias can serve as a predictor and classifier of various genomic and evolutionary traits across the domains of life. We perform secondary analysis of existing genetic datasets to build severa...

ModelRevelator: Fast phylogenetic model estimation via deep learning.

Molecular phylogenetics and evolution
Selecting the best model of sequence evolution for a multiple-sequence-alignment (MSA) constitutes the first step of phylogenetic tree reconstruction. Common approaches for inferring nucleotide models typically apply maximum likelihood (ML) methods, ...

Towards in silico CLIP-seq: predicting protein-RNA interaction via sequence-to-signal learning.

Genome biology
We present RBPNet, a novel deep learning method, which predicts CLIP-seq crosslink count distribution from RNA sequence at single-nucleotide resolution. By training on up to a million regions, RBPNet achieves high generalization on eCLIP, iCLIP and m...

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...

Deep learning of human polyadenylation sites at nucleotide resolution reveals molecular determinants of site usage and relevance in disease.

Nature communications
The genomic distribution of cleavage and polyadenylation (polyA) sites should be co-evolutionally optimized with the local gene structure. Otherwise, spurious polyadenylation can cause premature transcription termination and generate aberrant protein...

TransRNAm: Identifying Twelve Types of RNA Modifications by an Interpretable Multi-Label Deep Learning Model Based on Transformer.

IEEE/ACM transactions on computational biology and bioinformatics
Accurate identification of RNA modification sites is of great significance in understanding the functions and regulatory mechanisms of RNAs. Recent advances have shown great promise in applying computational methods based on deep learning for accurat...

miTDS: Uncovering miRNA-mRNA interactions with deep learning for functional target prediction.

Methods (San Diego, Calif.)
MicroRNAs (miRNAs) are vital in regulating gene expression through binding to specific target sites on messenger RNAs (mRNAs), a process closely tied to cancer pathogenesis. Identifying miRNA functional targets is essential but challenging, due to in...

DiCleave: a deep learning model for predicting human Dicer cleavage sites.

BMC bioinformatics
BACKGROUND: MicroRNAs (miRNAs) are a class of non-coding RNAs that play a pivotal role as gene expression regulators. These miRNAs are typically approximately 20 to 25 nucleotides long. The maturation of miRNAs requires Dicer cleavage at specific sit...

CADD v1.7: using protein language models, regulatory CNNs and other nucleotide-level scores to improve genome-wide variant predictions.

Nucleic acids research
Machine Learning-based scoring and classification of genetic variants aids the assessment of clinical findings and is employed to prioritize variants in diverse genetic studies and analyses. Combined Annotation-Dependent Depletion (CADD) is one of th...