Article

Sequential regulatory activity prediction across chromosomes with convolutional neural networks

2017-07-10

Abstract excerpt

Models for predicting phenotypic outcomes from genotypes have important applications to understanding genomic function and improving human health. Here, we develop a machine-learning system to predict cell type-specific epigenetic and transcriptional profiles in large mammalian genomes from DNA sequence alone. Using convolutional neural networks, this system identifies promoters and distal regulatory elements and...

Topics

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

Identifiers and source

Literature Corpus work: 333d4684-28c2-513d-b284-a0b277f7f938
DOI: 10.1101/161851

Open publication

Related research

Semantic proximity does not establish scientific evidence.

Click a neighbor to travelStep 1 · 12 closest

Sequential regulatory activity prediction across chromosomes with convolutional neural networksDOI 10.1101/161851Read

Sequential regulatory activity prediction across chromosomes with convolutional neural networks.Semantic proximity Read

A sequence-based global map of regulatory activity for deciphering human geneticsSemantic proximity Read

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

Fundamentals for predicting transcriptional regulations from DNA sequence patterns.Semantic proximity Read

Accurate promoter and enhancer identification in 127 ENCODE and Roadmap Epigenomics cell types and tissues by GenoSTANSemantic proximity Read

Predicting regulatory variants using a dense epigenomic mapped CNN model elucidated the molecular basis of trait-tissue associations.Semantic proximity Read

Predicting cell type-specific epigenomic profiles accounting for distal genetic effects.Semantic proximity Read

Genome-Wide Prediction of <i>cis</i> -Regulatory Regions Using Supervised Deep Learning MethodsSemantic proximity Read

Harnessing machine learning models for epigenome to transcriptome association studiesSemantic proximity Read

Current genomic deep learning models display decreased performance in cell type specific accessible regionsSemantic proximity Read

Deep learning approaches for non-coding genetic variant effect prediction: current progress and future prospects.Semantic proximity Read

Predicting mRNA abundance directly from genomic sequence using deep convolutional neural networksSemantic proximity Read