AIMC Topic: DNA

Clear Filters Showing 121 to 130 of 481 articles

Evaluation of DNA-protein complex structures using the deep learning method.

Physical chemistry chemical physics : PCCP
Biological processes such as transcription, repair, and regulation require interactions between DNA and proteins. To unravel their functions, it is imperative to determine the high-resolution structures of DNA-protein complexes. However, experimental...

Hold out the genome: a roadmap to solving the cis-regulatory code.

Nature
Gene expression is regulated by transcription factors that work together to read cis-regulatory DNA sequences. The 'cis-regulatory code' - how cells interpret DNA sequences to determine when, where and how much genes should be expressed - has proven ...

Toward three-dimensional DNA industrial nanorobots.

Science robotics
Nanoscale industrial robots have potential as manufacturing platforms and are capable of automatically performing repetitive tasks to handle and produce nanomaterials with consistent precision and accuracy. We demonstrate a DNA industrial nanorobot t...

Self-supervised Learning for DNA sequences with circular dilated convolutional networks.

Neural networks : the official journal of the International Neural Network Society
DNA molecules commonly exhibit wide interactions between the nucleobases. Modeling the interactions is important for obtaining accurate sequence-based inference. Although many deep learning methods have recently been developed for modeling DNA sequen...

Benchmarking of deep neural networks for predicting personal gene expression from DNA sequence highlights shortcomings.

Nature genetics
Deep learning methods have recently become the state of the art in a variety of regulatory genomic tasks, including the prediction of gene expression from genomic DNA. As such, these methods promise to serve as important tools in interpreting the ful...

Neural network execution using nicked DNA and microfluidics.

PloS one
DNA has been discussed as a potential medium for data storage. Potentially it could be denser, could consume less energy, and could be more durable than conventional storage media such as hard drives, solid-state storage, and optical media. However, ...

Deep-LASI: deep-learning assisted, single-molecule imaging analysis of multi-color DNA origami structures.

Nature communications
Single-molecule experiments have changed the way we explore the physical world, yet data analysis remains time-consuming and prone to human bias. Here, we introduce Deep-LASI (Deep-Learning Assisted Single-molecule Imaging analysis), a software suite...

Improving Enhancer Identification with a Multi-Classifier Stacked Ensemble Model.

Journal of molecular biology
Enhancers are DNA regions that are responsible for controlling the expression of genes. Enhancers are usually found upstream or downstream of a gene, or even inside a gene's intron region, but are normally located at a distant location from the genes...

KDeep: a new memory-efficient data extraction method for accurately predicting DNA/RNA transcription factor binding sites.

Journal of translational medicine
This paper addresses the crucial task of identifying DNA/RNA binding sites, which has implications in drug/vaccine design, protein engineering, and cancer research. Existing methods utilize complex neural network structures, diverse input types, and ...

A Machine-Learning-Based Approach to Prediction of Biogeographic Ancestry within Europe.

International journal of molecular sciences
Data obtained with the use of massive parallel sequencing (MPS) can be valuable in population genetics studies. In particular, such data harbor the potential for distinguishing samples from different populations, especially from those coming from adj...