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Regulatory Sequences, Nucleic Acid

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Convolutional neural network model to predict causal risk factors that share complex regulatory features.

Nucleic acids research
Major progress in disease genetics has been made through genome-wide association studies (GWASs). One of the key tasks for post-GWAS analyses is to identify causal noncoding variants with regulatory function. Here, on the basis of >2000 functional fe...

Predicting gene regulatory regions with a convolutional neural network for processing double-strand genome sequence information.

PloS one
With advances in sequencing technology, a vast amount of genomic sequence information has become available. However, annotating biological functions particularly of non-protein-coding regions in genome sequences without experiments is still a challen...

Large-scale DNA-based phenotypic recording and deep learning enable highly accurate sequence-function mapping.

Nature communications
Predicting effects of gene regulatory elements (GREs) is a longstanding challenge in biology. Machine learning may address this, but requires large datasets linking GREs to their quantitative function. However, experimental methods to generate such d...

Frequency spectra characterization of noncoding human genomic sequences.

Genes & genomics
BACKGROUND: Noncoding sequences have been demonstrated to possess regulatory functions. Its classification is challenging because they do not show well-defined nucleotide patterns that can correlate with their biological functions. Genomic signal pro...

Deep learning of immune cell differentiation.

Proceedings of the National Academy of Sciences of the United States of America
Although we know many sequence-specific transcription factors (TFs), how the DNA sequence of cis-regulatory elements is decoded and orchestrated on the genome scale to determine immune cell differentiation is beyond our grasp. Leveraging a granular a...

Prediction of enhancer-promoter interactions using the cross-cell type information and domain adversarial neural network.

BMC bioinformatics
BACKGROUND: Enhancer-promoter interactions (EPIs) play key roles in transcriptional regulation and disease progression. Although several computational methods have been developed to predict such interactions, their performances are not satisfactory w...

Deep learning suggests that gene expression is encoded in all parts of a co-evolving interacting gene regulatory structure.

Nature communications
Understanding the genetic regulatory code governing gene expression is an important challenge in molecular biology. However, how individual coding and non-coding regions of the gene regulatory structure interact and contribute to mRNA expression leve...

Sequence-Based Deep Learning Frameworks on Enhancer-Promoter Interactions Prediction.

Current pharmaceutical design
Enhancer-promoter interactions (EPIs) in the human genome are of great significance to transcriptional regulation, which tightly controls gene expression. Identification of EPIs can help us better decipher gene regulation and understand disease mecha...

Deep learning-based enhancement of epigenomics data with AtacWorks.

Nature communications
ATAC-seq is a widely-applied assay used to measure genome-wide chromatin accessibility; however, its ability to detect active regulatory regions can depend on the depth of sequencing coverage and the signal-to-noise ratio. Here we introduce AtacWorks...

DeepATT: a hybrid category attention neural network for identifying functional effects of DNA sequences.

Briefings in bioinformatics
Quantifying DNA properties is a challenging task in the broad field of human genomics. Since the vast majority of non-coding DNA is still poorly understood in terms of function, this task is particularly important to have enormous benefit for biology...