AIMC Topic: DNA

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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...

A machine learning technique for identifying DNA enhancer regions utilizing CIS-regulatory element patterns.

Scientific reports
Enhancers regulate gene expression, by playing a crucial role in the synthesis of RNAs and proteins. They do not directly encode proteins or RNA molecules. In order to control gene expression, it is important to predict enhancers and their potency. G...

Improving the prediction of DNA-protein binding by integrating multi-scale dense convolutional network with fault-tolerant coding.

Analytical biochemistry
Accurate prediction of DNA-protein binding (DPB) is of great biological significance for studying the regulatory mechanism of gene expression. In recent years, with the rapid development of deep learning techniques, advanced deep neural networks have...

Taxonomic classification of DNA sequences beyond sequence similarity using deep neural networks.

Proceedings of the National Academy of Sciences of the United States of America
Taxonomic classification, that is, the assignment to biological clades with shared ancestry, is a common task in genetics, mainly based on a genome similarity search of large genome databases. The classification quality depends heavily on the databas...

iDRBP-ECHF: Identifying DNA- and RNA-binding proteins based on extensible cubic hybrid framework.

Computers in biology and medicine
Proteins interact with nucleic acids to regulate the life activities of organisms. Therefore, how to accurately and efficiently identify nucleic acid-binding proteins (NABPs) is particularly significant. Some sequence-based computational methods have...