AIMC Topic: DNA

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Nonlinear manipulation and analysis of large DNA datasets.

Nucleic acids research
Information processing functions are essential for organisms to perceive and react to their complex environment, and for humans to analyze and rationalize them. While our brain is extraordinary at processing complex information, winner-take-all, as a...

Intelligent dynamic data perturbation OCDM encryption scheme based on cellular neural network and biological genetic encoding.

Optics express
In this paper, an intelligent dynamic perturbation orthogonal chirp division multiplexing (OCDM) encryption scheme based on cellular neural network and biological genetic encoding for seven-core optical fiber is proposed for the first time to our kno...

A tool for feature extraction from biological sequences.

Briefings in bioinformatics
With the advances in sequencing technologies, a huge amount of biological data is extracted nowadays. Analyzing this amount of data is beyond the ability of human beings, creating a splendid opportunity for machine learning methods to grow. The metho...

DNAcycP: a deep learning tool for DNA cyclizability prediction.

Nucleic acids research
DNA mechanical properties play a critical role in every aspect of DNA-dependent biological processes. Recently a high throughput assay named loop-seq has been developed to quantify the intrinsic bendability of a massive number of DNA fragments simult...

BERT6mA: prediction of DNA N6-methyladenine site using deep learning-based approaches.

Briefings in bioinformatics
N6-methyladenine (6mA) is associated with important roles in DNA replication, DNA repair, transcription, regulation of gene expression. Several experimental methods were used to identify DNA modifications. However, these experimental methods are cost...

Diffractive deep neural network adjoint assist or (DNA): a fast and efficient nonlinear diffractive neural network implementation.

Optics express
The recent advent of diffractive deep neural networks or DNNs has opened new avenues for the design and optimization of multi-functional optical materials; despite the effectiveness of the DNN approach, there is a need for making these networks as we...

DeepMP: a deep learning tool to detect DNA base modifications on Nanopore sequencing data.

Bioinformatics (Oxford, England)
MOTIVATION: DNA methylation plays a key role in a variety of biological processes. Recently, Nanopore long-read sequencing has enabled direct detection of these modifications. As a consequence, a range of computational methods have been developed to ...

Detection of transcription factors binding to methylated DNA by deep recurrent neural network.

Briefings in bioinformatics
Transcription factors (TFs) are proteins specifically involved in gene expression regulation. It is generally accepted in epigenetics that methylated nucleotides could prevent the TFs from binding to DNA fragments. However, recent studies have confir...

DeepDISOBind: accurate prediction of RNA-, DNA- and protein-binding intrinsically disordered residues with deep multi-task learning.

Briefings in bioinformatics
Proteins with intrinsically disordered regions (IDRs) are common among eukaryotes. Many IDRs interact with nucleic acids and proteins. Annotation of these interactions is supported by computational predictors, but to date, only one tool that predicts...

Deciphering the regulatory syntax of genomic DNA with deep learning.

Journal of biosciences
An organism's genome contains many sequence regions that perform diverse functions. Examples of such regions include genes, promoters, enhancers, and binding sites for regulatory proteins and RNAs. One of biology's most important open problems is how...