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Enhancer Elements, Genetic

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eRFSVM: a hybrid classifier to predict enhancers-integrating random forests with support vector machines.

Hereditas
BACKGROUND: Enhancers are tissue specific distal regulation elements, playing vital roles in gene regulation and expression. The prediction and identification of enhancers are important but challenging issues for bioinformatics studies. Existing comp...

Opening up the blackbox: an interpretable deep neural network-based classifier for cell-type specific enhancer predictions.

BMC systems biology
BACKGROUND: Gene expression is mediated by specialized cis-regulatory modules (CRMs), the most prominent of which are called enhancers. Early experiments indicated that enhancers located far from the gene promoters are often responsible for mediating...

EP-DNN: A Deep Neural Network-Based Global Enhancer Prediction Algorithm.

Scientific reports
We present EP-DNN, a protocol for predicting enhancers based on chromatin features, in different cell types. Specifically, we use a deep neural network (DNN)-based architecture to extract enhancer signatures in a representative human embryonic stem c...

Sequence based predictor for discrimination of enhancer and their types by applying general form of Chou's trinucleotide composition.

Computer methods and programs in biomedicine
BACKGROUND AND OBJECTIVES: Enhancers are pivotal DNA elements, which are widely used in eukaryotes for activation of transcription genes. On the basis of enhancer strength, they are further classified into two groups; strong enhancers and weak enhanc...

McEnhancer: predicting gene expression via semi-supervised assignment of enhancers to target genes.

Genome biology
Transcriptional enhancers regulate spatio-temporal gene expression. While genomic assays can identify putative enhancers en masse, assigning target genes is a complex challenge. We devised a machine learning approach, McEnhancer, which links target g...

ClusterTAD: an unsupervised machine learning approach to detecting topologically associated domains of chromosomes from Hi-C data.

BMC bioinformatics
BACKGROUND: With the development of chromosomal conformation capturing techniques, particularly, the Hi-C technique, the study of the spatial conformation of a genome is becoming an important topic in bioinformatics and computational biology. The Hi-...

Predicting enhancers with deep convolutional neural networks.

BMC bioinformatics
BACKGROUND: With the rapid development of deep sequencing techniques in the recent years, enhancers have been systematically identified in such projects as FANTOM and ENCODE, forming genome-wide landscapes in a series of human cell lines. Nevertheles...

A survey of recently emerged genome-wide computational enhancer predictor tools.

Computational biology and chemistry
The race for the discovery of enhancers at a genome-wide scale has been on since the commencement of next generation sequencing decades after the discovery of the first enhancer, SV40. A few enhancer-predicting features such as chromatin feature, his...