Article

Discovery of a non-canonical GRHL1 binding site using deep convolutional and recurrent neural networks

2022-07-01

Abstract excerpt

<h4>ABSTRACT</h4> Transcription factors regulate gene expression by binding to transcription factor binding sites (TFBSs). Most models for predicting TFBSs are based on position weight matrices (PWMs), which require a specific motif to be present in the DNA sequence and do not consider interdependencies of nucleotides. Novel approaches such as Transcription Factor Flexible Models or recurrent neural networks cons...

Topics

Open a Topic to create a Post that cites this publication.

Identifiers and source

Literature Corpus work: dcd6d992-e441-580e-a111-6e99a6afec46
DOI: 10.1101/2022.06.28.497553

Open publication

Related research

Semantic proximity does not establish scientific evidence.

Click a neighbor to travelStep 1 · 12 closest

Discovery of a non-canonical GRHL1 binding site using deep convolutional and recurrent neural networksDOI 10.1101/2022.06.28.497553Read

Systematic Prediction of Regulatory Motifs from Human ChIP-Sequencing Data Based on a Deep Learning FrameworkSemantic proximity Read

Using attentive gated neural networks to quantify the impact of non-coding variants on transcription factor binding affinitySemantic proximity Read

Virtual ChIP-seq: predicting transcription factor binding by learning from the transcriptomeSemantic proximity Read

DanQ: a hybrid convolutional and recurrent deep neural network for quantifying the function of DNA sequences.Semantic proximity Read

Discovery of Primary, Cofactor, and Novel Transcription Factor Binding Site Motifs by Recursive, Thresholded Entropy MinimizationSemantic proximity Read

Explain-seq: an end-to-end pipeline from training to interpretation of sequence-based deep learning modelsSemantic proximity Read

Base-resolution prediction of transcription factor binding signals by a deep learning frameworkSemantic proximity Read

DanQ: a hybrid convolutional and recurrent deep neural network for quantifying the function of DNA sequencesSemantic proximity Read

Classic machine learning on top of multiple position weight matrices improves genomic prediction of transcription factor binding sitesSemantic proximity Read

Comparative analysis of models in predicting the effects of SNPs on TF-DNA binding using large-scale in vitro and in vivo data.Semantic proximity Read

Graph Neural Networks for Z-DNA prediction in GenomesSemantic proximity Read

Comparative Analysis of Deep Learning Models for Predicting Causative Regulatory VariantsSemantic proximity Read