AIMC Topic: DNA

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ReQuant: improved base modification calling by k-mer value imputation.

Nucleic acids research May 10, 2025
Nanopore sequencing allows identification of base modifications, such as methylation, directly from raw current data. Prevailing approaches, including deep learning (DL) methods, require training data covering all possible sequence contexts. These da...
Software Algorithms Deep Learning Humans CpG Islands Bacteriophage lambda Nanopore Sequencing DNA Methylation DNA Sequence Analysis, DNA
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PCVR: a pre-trained contextualized visual representation for DNA sequence classification.

BMC bioinformatics May 9, 2025
BACKGROUND: The classification of DNA sequences is pivotal in bioinformatics, essentially for genetic information analysis. Traditional alignment-based tools tend to have slow speed and low recall. Machine learning methods learn implicit patterns fro...
Computational Biology Machine Learning Sequence Analysis, DNA Algorithms DNA
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Identifying the DNA methylation preference of transcription factors using ProtBERT and SVM.

PLoS computational biology May 1, 2025
Transcription factors (TFs) can affect gene expression by binding to certain specific DNA sequences. This binding process of TFs may be modulated by DNA methylation. A subset of TFs that serve as methylation readers preferentially binds to certain me...
DNA Mice Computational Biology Support Vector Machine DNA Methylation Transcription Factors Humans Algorithms Animals
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iProtDNA-SMOTE: Enhancing protein-DNA binding sites prediction through imbalanced graph neural networks.

PloS one Jan 1, 2025
Protein-DNA interactions play a crucial role in cellular biology, essential for maintaining life processes and regulating cellular functions. We propose a method called iProtDNA-SMOTE, which utilizes non-equilibrium graph neural networks along with p...
Neural Networks, Computer Software Databases, Protein DNA DNA-Binding Proteins Protein Binding Computational Biology Algorithms Binding Sites Graph Neural Networks
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Towards the genomic sequence code of DNA fragility for machine learning.

Nucleic acids research Nov 27, 2024
Genomic DNA breakages and the subsequent insertion and deletion mutations are important contributors to genome instability and linked diseases. Unlike the research in point mutations, the relationship between DNA sequence context and the propensity f...
DNA Breaks Genome, Human Genomic Instability Humans Machine Learning Genomics DNA Neoplasms
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A review of deep learning models for the prediction of chromatin interactions with DNA and epigenomic profiles.

Briefings in bioinformatics Nov 22, 2024
Advances in three-dimensional (3D) genomics have revealed the spatial characteristics of chromatin interactions in gene expression regulation, which is crucial for understanding molecular mechanisms in biological processes. High-throughput technologi...
Epigenomics DNA Chromatin Computational Biology Epigenesis, Genetic Deep Learning Genomics Humans
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Precision DNA methylation typing via hierarchical clustering of Nanopore current signals and attention-based neural network.

Briefings in bioinformatics Sep 23, 2024
Decoding DNA methylation sites through nanopore sequencing has emerged as a cutting-edge technology in the field of DNA methylation research, as it enables direct sequencing of native DNA molecules without the need for prior enzymatic or chemical tre...
Nanopore Sequencing Humans DNA Neural Networks, Computer DNA Methylation Nanopores Sequence Analysis, DNA Cluster Analysis
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Predicting bacterial transcription factor binding sites through machine learning and structural characterization based on DNA duplex stability.

Briefings in bioinformatics Sep 23, 2024
Transcriptional factors (TFs) in bacteria play a crucial role in gene regulation by binding to specific DNA sequences, thereby assisting in the activation or repression of genes. Despite their central role, deciphering shape recognition of bacterial ...
Machine Learning Nucleic Acid Conformation Algorithms Binding Sites Computational Biology DNA Protein Binding DNA, Bacterial Bacterial Proteins Bacteria Transcription Factors
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MLSNet: a deep learning model for predicting transcription factor binding sites.

Briefings in bioinformatics Sep 23, 2024
Accurate prediction of transcription factor binding sites (TFBSs) is essential for understanding gene regulation mechanisms and the etiology of diseases. Despite numerous advances in deep learning for predicting TFBSs, their performance can still be ...
Algorithms Deep Learning Transcription Factors DNA Computational Biology Humans Binding Sites Chromatin Immunoprecipitation Sequencing
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An integrated machine-learning model to predict nucleosome architecture.

Nucleic acids research Sep 23, 2024
We demonstrate that nucleosomes placed in the gene body can be accurately located from signal decay theory assuming two emitters located at the beginning and at the end of genes. These generated wave signals can be in phase (leading to well defined n...
Humans Binding Sites Machine Learning DNA Nucleosomes Transcription Factors
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