AIMC Topic: Nucleotides

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

Methods (San Diego, Calif.) Jan 26, 2024
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...
RNA Editing MicroRNAs Nucleotides Deep Learning RNA, Messenger
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DiCleave: a deep learning model for predicting human Dicer cleavage sites.

BMC bioinformatics Jan 9, 2024
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...
Humans MicroRNAs Benchmarking Nucleotides Machine Learning Deep Learning
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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 Dec 25, 2023
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...
Nucleotides RNA Neural Networks, Computer Deep Learning
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Deep learning of human polyadenylation sites at nucleotide resolution reveals molecular determinants of site usage and relevance in disease.

Nature communications Nov 15, 2023
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...
Deep Learning Nucleotides Genome, Human Humans Transcription, Genetic Polyadenylation
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ModelRevelator: Fast phylogenetic model estimation via deep learning.

Molecular phylogenetics and evolution Aug 16, 2023
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, ...
Phylogeny Likelihood Functions Sequence Alignment Deep Learning Nucleotides
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Towards in silico CLIP-seq: predicting protein-RNA interaction via sequence-to-signal learning.

Genome biology Aug 4, 2023
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...
Nucleotide Motifs RNA Splice Sites Base Sequence Binding Sites Consensus Sequence Alleles Nucleotides RNA Datasets as Topic Internet RNA, Viral Humans Computer Simulation Bias RNA, Messenger Mutation RNA-Binding Proteins Deep Learning
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How Deepbics Quantifies Intensities of Transcription Factor-DNA Binding and Facilitates Prediction of Single Nucleotide Variant Pathogenicity With a Deep Learning Model Trained On ChIP-Seq Data Sets.

IEEE/ACM transactions on computational biology and bioinformatics Apr 3, 2023
The binding of DNA sequences to cell type-specific transcription factors is essential for regulating gene expression in all organisms. Many variants occurring in these binding regions play crucial roles in human disease by disrupting the cis-regulati...
Nucleotides Binding Sites Chromatin Immunoprecipitation Sequencing DNA Virulence Deep Learning Transcription Factors Humans
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Machine learning classifiers predict key genomic and evolutionary traits across the kingdoms of life.

Scientific reports Feb 6, 2023
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...
Machine Learning Reproducibility of Results Nucleotides Phylogeny Codon Genomics
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Predicting RNA secondary structure by a neural network: what features may be learned?

PeerJ Dec 13, 2022
Deep learning is a class of machine learning techniques capable of creating internal representation of data without explicit preprogramming. Hence, in addition to practical applications, it is of interest to analyze what features of biological data m...
RNA Machine Learning Nucleotides Base Pairing Humans Neural Networks, Computer
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MetaRNN: differentiating rare pathogenic and rare benign missense SNVs and InDels using deep learning.

Genome medicine Oct 8, 2022
Multiple computational approaches have been developed to improve our understanding of genetic variants. However, their ability to identify rare pathogenic variants from rare benign ones is still lacking. Using context annotations and deep learning me...
Deep Learning Nucleotides Computational Biology INDEL Mutation
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