AIMC Topic: DNA

Clear Filters Showing 441 to 450 of 457 articles

Outgroup Machine Learning Approach Identifies Single Nucleotide Variants in Noncoding DNA Associated with Autism Spectrum Disorder.

Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing Jan 1, 2019
Autism spectrum disorder (ASD) is a heritable neurodevelopmental disorder affecting 1 in 59 children. While noncoding genetic variation has been shown to play a major role in many complex disorders, the contribution of these regions to ASD susceptibi...
Humans Machine Learning CpG Islands RNA, Untranslated Microsatellite Repeats Supranuclear Palsy, Progressive DNA Whole Genome Sequencing Genetic Variation Gene Regulatory Networks Genetic Predisposition to Disease Polymorphism, Single Nucleotide Genetic Association Studies Computational Biology Logistic Models MicroRNAs Male Phenotype Case-Control Studies Cohort Studies Autism Spectrum Disorder Child Female
View on PubMed

DNA Steganalysis Using Deep Recurrent Neural Networks.

Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing Jan 1, 2019
Recent advances in next-generation sequencing technologies have facilitated the use of deoxyribonucleic acid (DNA) as a novel covert channels in steganography. There are various methods that exist in other domains to detect hidden messages in convent...
Sequence Analysis, DNA DNA Information Theory Communications Media Humans Neural Networks, Computer Deep Learning Computational Biology
View on PubMed

Discovering epistatic feature interactions from neural network models of regulatory DNA sequences.

Bioinformatics (Oxford, England) Sep 1, 2018
MOTIVATION: Transcription factors bind regulatory DNA sequences in a combinatorial manner to modulate gene expression. Deep neural networks (DNNs) can learn the cis-regulatory grammars encoded in regulatory DNA sequences associated with transcription...
Neural Networks, Computer Protein Binding Genomics Transcription Factors Binding Sites Chromatin Sequence Analysis, DNA DNA High-Throughput Nucleotide Sequencing
View on PubMed DOI

DeFine: deep convolutional neural networks accurately quantify intensities of transcription factor-DNA binding and facilitate evaluation of functional non-coding variants.

Nucleic acids research Jun 20, 2018
The complex system of gene expression is regulated by the cell type-specific binding of transcription factors (TFs) to regulatory elements. Identifying variants that disrupt TF binding and lead to human diseases remains a great challenge. To address ...
Binding Sites Regulatory Elements, Transcriptional Regulatory Sequences, Nucleic Acid Neural Networks, Computer Algorithms Computational Biology Transcription Factors DNA DNA-Binding Proteins Gene Expression Regulation Humans
View on PubMed DOI

LeNup: learning nucleosome positioning from DNA sequences with improved convolutional neural networks.

Bioinformatics (Oxford, England) May 15, 2018
MOTIVATION: Nucleosome positioning plays significant roles in proper genome packing and its accessibility to execute transcription regulation. Despite a multitude of nucleosome positioning resources available on line including experimental datasets o...
Genome Chromatin Assembly and Disassembly Nucleosomes Saccharomyces cerevisiae Drosophila melanogaster Caenorhabditis elegans Humans Neural Networks, Computer Software Algorithms Animals DNA
View on PubMed DOI

Modeling positional effects of regulatory sequences with spline transformations increases prediction accuracy of deep neural networks.

Bioinformatics (Oxford, England) Apr 15, 2018
MOTIVATION: Regulatory sequences are not solely defined by their nucleic acid sequence but also by their relative distances to genomic landmarks such as transcription start site, exon boundaries or polyadenylation site. Deep learning has become the a...
Humans Neural Networks, Computer Machine Learning Software Protein Binding Genomics K562 Cells Hep G2 Cells Regulatory Sequences, Nucleic Acid Sequence Analysis, RNA Proteins Models, Genetic DNA Sequence Analysis, DNA RNA
View on PubMed DOI

Survey of Computational Approaches for Prediction of DNA-Binding Residues on Protein Surfaces.

Methods in molecular biology (Clifton, N.J.) Jan 1, 2018
The increasing number of protein structures with uncharacterized function necessitates the development of in silico prediction methods for functional annotations on proteins. In this chapter, different kinds of computational approaches are briefly in...
Sequence Analysis, Protein DNA-Binding Proteins Amino Acid Sequence Protein Conformation Binding Sites Computational Biology Databases, Protein DNA Machine Learning Molecular Docking Simulation Protein Binding
View on PubMed DOI

DNA sequence+shape kernel enables alignment-free modeling of transcription factor binding.

Bioinformatics (Oxford, England) Oct 1, 2017
MOTIVATION: Transcription factors (TFs) bind to specific DNA sequence motifs. Several lines of evidence suggest that TF-DNA binding is mediated in part by properties of the local DNA shape: the width of the minor groove, the relative orientations of ...
Sequence Analysis, DNA Protein Binding Nucleic Acid Conformation DNA Genomics Software Protein Array Analysis DNA-Binding Proteins Transcription Factors Binding Sites Support Vector Machine Nucleotide Motifs
View on PubMed DOI

DIRECTION: a machine learning framework for predicting and characterizing DNA methylation and hydroxymethylation in mammalian genomes.

Bioinformatics (Oxford, England) Oct 1, 2017
MOTIVATION: 5-Methylcytosine and 5-Hydroxymethylcytosine in DNA are major epigenetic modifications known to significantly alter mammalian gene expression. High-throughput assays to detect these modifications are expensive, labor-intensive, unfeasible...
Algorithms Animals Epigenesis, Genetic DNA Methylation 5-Methylcytosine Humans Machine Learning Software DNA Mammals CpG Islands
View on PubMed DOI

DNA robots sort as they walk.

Science (New York, N.Y.) Sep 15, 2017
Robotics DNA
View on PubMed DOI
Popular Topics
Recent Journals