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DNA

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High-performance deep learning pipeline predicts individuals in mixtures of DNA using sequencing data.

Briefings in bioinformatics
In this study, we proposed a deep learning (DL) model for classifying individuals from mixtures of DNA samples using 27 short tandem repeats and 94 single nucleotide polymorphisms obtained through massively parallel sequencing protocol. The model was...

SAResNet: self-attention residual network for predicting DNA-protein binding.

Briefings in bioinformatics
Knowledge of the specificity of DNA-protein binding is crucial for understanding the mechanisms of gene expression, regulation and gene therapy. In recent years, deep-learning-based methods for predicting DNA-protein binding from sequence data have a...

iDHS-Deep: an integrated tool for predicting DNase I hypersensitive sites by deep neural network.

Briefings in bioinformatics
DNase I hypersensitive site (DHS) refers to the hypersensitive region of chromatin for the DNase I enzyme. It is an important part of the noncoding region and contains a variety of regulatory elements, such as promoter, enhancer, and transcription fa...

A transformer architecture based on BERT and 2D convolutional neural network to identify DNA enhancers from sequence information.

Briefings in bioinformatics
Recently, language representation models have drawn a lot of attention in the natural language processing field due to their remarkable results. Among them, bidirectional encoder representations from transformers (BERT) has proven to be a simple, yet...

Bound2Learn: a machine learning approach for classification of DNA-bound proteins from single-molecule tracking experiments.

Nucleic acids research
DNA-bound proteins are essential elements for the maintenance, regulation, and use of the genome. The time they spend bound to DNA provides useful information on their stability within protein complexes and insight into the understanding of biologica...

Optical microlever assisted DNA stretching.

Optics express
Optical microrobotics is an emerging field that has the potential to improve upon current optical tweezer studies through avenues such as limiting the exposure of biological molecules of interest to laser radiation and overcoming the current limitati...

dSPRINT: predicting DNA, RNA, ion, peptide and small molecule interaction sites within protein domains.

Nucleic acids research
Domains are instrumental in facilitating protein interactions with DNA, RNA, small molecules, ions and peptides. Identifying ligand-binding domains within sequences is a critical step in protein function annotation, and the ligand-binding properties ...

A survey on deep learning in DNA/RNA motif mining.

Briefings in bioinformatics
DNA/RNA motif mining is the foundation of gene function research. The DNA/RNA motif mining plays an extremely important role in identifying the DNA- or RNA-protein binding site, which helps to understand the mechanism of gene regulation and managemen...

keras_dna: a wrapper for fast implementation of deep learning models in genomics.

Bioinformatics (Oxford, England)
SUMMARY: Prediction of genomic annotations from DNA sequences using deep learning is today becoming a flourishing field with many applications. Nevertheless, there are still difficulties in handling data in order to conveniently build and train model...

Enabling autonomous scanning probe microscopy imaging of single molecules with deep learning.

Nanoscale
Scanning probe microscopies allow investigating surfaces at the nanoscale, in real space and with unparalleled signal-to-noise ratio. However, these microscopies are not used as much as it would be expected considering their potential. The main limit...