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Enhancer Elements, Genetic

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Cell-type-directed design of synthetic enhancers.

Nature
Transcriptional enhancers act as docking stations for combinations of transcription factors and thereby regulate spatiotemporal activation of their target genes. It has been a long-standing goal in the field to decode the regulatory logic of an enhan...

Targeted design of synthetic enhancers for selected tissues in the Drosophila embryo.

Nature
Enhancers control gene expression and have crucial roles in development and homeostasis. However, the targeted de novo design of enhancers with tissue-specific activities has remained challenging. Here we combine deep learning and transfer learning t...

Enhancer-MDLF: a novel deep learning framework for identifying cell-specific enhancers.

Briefings in bioinformatics
Enhancers, noncoding DNA fragments, play a pivotal role in gene regulation, facilitating gene transcription. Identifying enhancers is crucial for understanding genomic regulatory mechanisms, pinpointing key elements and investigating networks governi...

Identification of drug responsive enhancers by predicting chromatin accessibility change from perturbed gene expression profiles.

NPJ systems biology and applications
Individual may response to drug treatment differently due to their genetic variants located in enhancers. These variants can alter transcription factor's (TF) binding strength, affect enhancer's chromatin activity or interaction, and eventually chang...

EPI-Trans: an effective transformer-based deep learning model for enhancer promoter interaction prediction.

BMC bioinformatics
BACKGROUND: Recognition of enhancer-promoter Interactions (EPIs) is crucial for human development. EPIs in the genome play a key role in regulating transcription. However, experimental approaches for classifying EPIs are too expensive in terms of eff...

Supervised learning of enhancer-promoter specificity based on genome-wide perturbation studies highlights areas for improvement in learning.

Bioinformatics (Oxford, England)
MOTIVATION: Understanding the rules that govern enhancer-driven transcription remains a central unsolved problem in genomics. Now with multiple massively parallel enhancer perturbation assays published, there are enough data that we can utilize to le...

Development of a novel prognostic signature derived from super-enhancer-associated gene by machine learning in head and neck squamous cell carcinoma.

Oral oncology
Dysregulated super-enhancer (SE) results in aberrant transcription that drives cancer initiation and progression. SEs have been demonstrated as novel promising diagnostic/prognostic biomarkers and therapeutic targets across multiple human cancers. He...

DeepDualEnhancer: A Dual-Feature Input DNABert Based Deep Learning Method for Enhancer Recognition.

International journal of molecular sciences
Enhancers are cis-regulatory DNA sequences that are widely distributed throughout the genome. They can precisely regulate the expression of target genes. Since the features of enhancer segments are difficult to detect, we propose DeepDualEnhancer, a ...

A novel interpretable deep learning-based computational framework designed synthetic enhancers with broad cross-species activity.

Nucleic acids research
Enhancers play a critical role in dynamically regulating spatial-temporal gene expression and establishing cell identity, underscoring the significance of designing them with specific properties for applications in biosynthetic engineering and gene t...

Machine and Deep Learning Methods for Predicting 3D Genome Organization.

Methods in molecular biology (Clifton, N.J.)
Three-dimensional (3D) chromatin interactions, such as enhancer-promoter interactions (EPIs), loops, topologically associating domains (TADs), and A/B compartments, play critical roles in a wide range of cellular processes by regulating gene expressi...