AIMC Topic: Enhancer Elements, Genetic

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Machine learning sequence prioritization for cell type-specific enhancer design.

eLife
Recent discoveries of extreme cellular diversity in the brain warrant rapid development of technologies to access specific cell populations within heterogeneous tissue. Available approaches for engineering-targeted technologies for new neuron subtype...

An explainable artificial intelligence approach for decoding the enhancer histone modifications code and identification of novel enhancers in Drosophila.

Genome biology
BACKGROUND: Enhancers are non-coding regions of the genome that control the activity of target genes. Recent efforts to identify active enhancers experimentally and in silico have proven effective. While these tools can predict the locations of enhan...

ReFeaFi: Genome-wide prediction of regulatory elements driving transcription initiation.

PLoS computational biology
Regulatory elements control gene expression through transcription initiation (promoters) and by enhancing transcription at distant regions (enhancers). Accurate identification of regulatory elements is fundamental for annotating genomes and understan...

iEnhancer-RD: Identification of enhancers and their strength using RKPK features and deep neural networks.

Analytical biochemistry
Enhancers are regulatory elements involved in gene expression.It is a part of DNA, which can enhance the transcription rate of gene. However, the identification of enhancer by biological experimental methods is time-consuming and expensive. Therefore...

Deep learning connects DNA traces to transcription to reveal predictive features beyond enhancer-promoter contact.

Nature communications
Chromatin architecture plays an important role in gene regulation. Recent advances in super-resolution microscopy have made it possible to measure chromatin 3D structure and transcription in thousands of single cells. However, leveraging these comple...

SeqEnhDL: sequence-based classification of cell type-specific enhancers using deep learning models.

BMC research notes
OBJECTIVE: To address the challenge of computational identification of cell type-specific regulatory elements on a genome-wide scale.

DeepCAPE: A Deep Convolutional Neural Network for the Accurate Prediction of Enhancers.

Genomics, proteomics & bioinformatics
The establishment of a landscape of enhancers across human cells is crucial to deciphering the mechanism of gene regulation, cell differentiation, and disease development. High-throughput experimental approaches, which contain successfully reported e...

ES-ARCNN: Predicting enhancer strength by using data augmentation and residual convolutional neural network.

Analytical biochemistry
Enhancers are non-coding DNA sequences bound by proteins called transcription factors. They function as distant regulators of gene transcription and participate in the development and maintenance of cell types and tissues. Since experimental validati...

Learning and interpreting the gene regulatory grammar in a deep learning framework.

PLoS computational biology
Deep neural networks (DNNs) have achieved state-of-the-art performance in identifying gene regulatory sequences, but they have provided limited insight into the biology of regulatory elements due to the difficulty of interpreting the complex features...