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DNA

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Mechano-fluorescence actuation in single synaptic vesicles with a DNA framework nanomachine.

Science robotics
Biomimetic machines that can convert mechanical actuation to adaptive coloration in a manner analogous to cephalopods have found widespread applications at various length scales. At the nanoscale, a transmutable nanomachine with adaptive colors that ...

Protein-DNA Binding Residue Prediction via Bagging Strategy and Sequence-Based Cube-Format Feature.

IEEE/ACM transactions on computational biology and bioinformatics
Protein-DNA interactions play an important role in diverse biological processes. Accurately identifying protein-DNA binding residues is a critical but challenging task for protein function annotations and drug design. Although wet-lab experimental me...

CNN-Pred: Prediction of single-stranded and double-stranded DNA-binding protein using convolutional neural networks.

Gene
DNA-binding proteins play a vital role in biological activity including DNA replication, DNA packing, and DNA reparation. DNA-binding proteins can be classified into single-stranded DNA-binding proteins (SSBs) or double-stranded DNA-binding proteins ...

iEnhancer-DCLA: using the original sequence to identify enhancers and their strength based on a deep learning framework.

BMC bioinformatics
Enhancers are small regions of DNA that bind to proteins, which enhance the transcription of genes. The enhancer may be located upstream or downstream of the gene. It is not necessarily close to the gene to be acted on, because the entanglement struc...

Current understanding of biological interactions and processing of DNA origami nanostructures: Role of machine learning and implications in drug delivery.

Biotechnology advances
DNA origami has emerged as an exciting avenue that provides a versatile two and three-dimensional DNA-based platform for nanomedicine and drug delivery applications. Their incredible programmability, custom synthesis, efficiency, biocompatibility, an...

Nonlinear decision-making with enzymatic neural networks.

Nature
Artificial neural networks have revolutionized electronic computing. Similarly, molecular networks with neuromorphic architectures may enable molecular decision-making on a level comparable to gene regulatory networks. Non-enzymatic networks could in...

Using Artificial Neural Networks to Model Errors in Biochemical Manipulation of DNA Molecules.

IEEE/ACM transactions on computational biology and bioinformatics
In recent years, the non-biological applications of DNA molecules have made considerable progress; most of these applications were performed in vitro, involving biochemical operations such as synthesis, amplification and sequencing. Because errors ma...

Artificial intelligence-augmented histopathologic review using image analysis to optimize DNA yield from formalin-fixed paraffin-embedded slides.

Modern pathology : an official journal of the United States and Canadian Academy of Pathology, Inc
To achieve minimum DNA input requirements for next-generation sequencing (NGS), pathologists visually estimate macrodissection and slide count decisions. Unfortunately, misestimation may cause tissue waste and increased laboratory costs. We developed...

DPProm: A Two-Layer Predictor for Identifying Promoters and Their Types on Phage Genome Using Deep Learning.

IEEE journal of biomedical and health informatics
With the number of phage genomes increasing, it is urgent to develop new bioinformatics methods for phage genome annotation. Promoter, a DNA region, is important for gene transcriptional regulation. In the era of post-genomics, the availability of da...

Context dependent prediction in DNA sequence using neural networks.

PeerJ
One way to better understand the structure in DNA is by learning to predict the sequence. Here, we trained a model to predict the missing base at any given position, given its left and right flanking contexts. Our best-performing model was a neural n...