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DNA

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Prediction of DNA origami shape using graph neural network.

Nature materials
Unlike proteins, which have a wealth of validated structural data, experimentally or computationally validated DNA origami datasets are limited. Here we present a graph neural network that can predict the three-dimensional conformation of DNA origami...

DeepSF-4mC: A deep learning model for predicting DNA cytosine 4mC methylation sites leveraging sequence features.

Computers in biology and medicine
N-methylcytosine (4mC) is a DNA modification involving the addition of a methyl group to the fourth nitrogen atom of the cytosine base. This modification may influence gene regulation, providing potential insights into gene control mechanisms. Tradit...

RUBICON: a framework for designing efficient deep learning-based genomic basecallers.

Genome biology
Nanopore sequencing generates noisy electrical signals that need to be converted into a standard string of DNA nucleotide bases using a computational step called basecalling. The performance of basecalling has critical implications for all later step...

Building an ab initio solvated DNA model using Euclidean neural networks.

PloS one
Accurately modeling large biomolecules such as DNA from first principles is fundamentally challenging due to the steep computational scaling of ab initio quantum chemistry methods. This limitation becomes even more prominent when modeling biomolecule...

Predicting DNA structure using a deep learning method.

Nature communications
Understanding the mechanisms of protein-DNA binding is critical in comprehending gene regulation. Three-dimensional DNA structure, also described as DNA shape, plays a key role in these mechanisms. In this study, we present a deep learning-based meth...

promSEMBLE: Hard Pattern Mining and Ensemble Learning for Detecting DNA Promoter Sequences.

IEEE/ACM transactions on computational biology and bioinformatics
Accurate identification of DNA promoter sequences is of crucial importance in unraveling the underlying mechanisms that regulate gene transcription. Initiation of transcription is controlled through regulatory transcription factors binding to promote...

DRBpred: A sequence-based machine learning method to effectively predict DNA- and RNA-binding residues.

Computers in biology and medicine
DNA-binding and RNA-binding proteins are essential to an organism's normal life cycle. These proteins have diverse functions in various biological processes. DNA-binding proteins are crucial for DNA replication, transcription, repair, packaging, and ...

Mesophilic Argonaute-Mediated Polydisperse Droplet Biosensor for Amplification-Free, One-Pot, and Multiplexed Nucleic Acid Detection Using Deep Learning.

Analytical chemistry
Detection of nucleic acids from a single multiplexed and amplification-free test is critical for ensuring food safety, clinical diagnostics, and environmental monitoring. In this study, we introduced a mesophilic Argonaute protein from (CbAgo), whic...

DARDN: A Deep-Learning Approach for CTCF Binding Sequence Classification and Oncogenic Regulatory Feature Discovery.

Genes
Characterization of gene regulatory mechanisms in cancer is a key task in cancer genomics. CCCTC-binding factor (CTCF), a DNA binding protein, exhibits specific binding patterns in the genome of cancer cells and has a non-canonical function to facili...

DBPboost:A method of classification of DNA-binding proteins based on improved differential evolution algorithm and feature extraction.

Methods (San Diego, Calif.)
DNA-binding proteins are a class of proteins that can interact with DNA molecules through physical and chemical interactions. Their main functions include regulating gene expression, maintaining chromosome structure and stability, and more. DNA-bindi...