AIMC Topic: Enhancer Elements, Genetic

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Endometrial tumorigenesis involves epigenetic plasticity demarcating non-coding somatic mutations and 3D-genome alterations.

Genome biology
BACKGROUND: The incidence and mortality of endometrial cancer (EC) is on the rise. Eighty-five percent of ECs depend on estrogen receptor alpha (ERα) for proliferation, but little is known about its transcriptional regulation in these tumors.

EPIPDLF: a pretrained deep learning framework for predicting enhancer-promoter interactions.

Bioinformatics (Oxford, England)
MOTIVATION: Enhancers and promoters, as regulatory DNA elements, play pivotal roles in gene expression, homeostasis, and disease development across various biological processes. With advancing research, it has been uncovered that distal enhancers may...

EnsembleSE: identification of super-enhancers based on ensemble learning.

Briefings in functional genomics
Super-enhancers (SEs) are typically located in the regulatory regions of genes, driving high-level gene expression. Identifying SEs is crucial for a deeper understanding of gene regulatory networks, disease mechanisms, and the development and physiol...

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...

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...

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...

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...

HEAP: a task adaptive-based explainable deep learning framework for enhancer activity prediction.

Briefings in bioinformatics
Enhancers are crucial cis-regulatory elements that control gene expression in a cell-type-specific manner. Despite extensive genetic and computational studies, accurately predicting enhancer activity in different cell types remains a challenge, and t...

DeepITEH: a deep learning framework for identifying tissue-specific eRNAs from the human genome.

Bioinformatics (Oxford, England)
MOTIVATION: Enhancers are vital cis-regulatory elements that regulate gene expression. Enhancer RNAs (eRNAs), a type of long noncoding RNAs, are transcribed from enhancer regions in the genome. The tissue-specific expression of eRNAs is crucial in th...

Analysis of super-enhancer using machine learning and its application to medical biology.

Briefings in bioinformatics
The analysis of super-enhancers (SEs) has recently attracted attention in elucidating the molecular mechanisms of cancer and other diseases. SEs are genomic structures that strongly induce gene expression and have been reported to contribute to the o...