AIMC Topic: Regulatory Sequences, Nucleic Acid

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

Nature communications Dec 1, 2020
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...
Mice Bacteria RNA, Messenger Humans Animals Deep Learning Saccharomyces cerevisiae Drosophila melanogaster Evolution, Molecular Regulatory Sequences, Nucleic Acid Gene Expression Regulation Base Sequence
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Prediction of enhancer-promoter interactions using the cross-cell type information and domain adversarial neural network.

BMC bioinformatics Nov 7, 2020
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...
Neural Networks, Computer ROC Curve Cell Line Area Under Curve Humans Promoter Regions, Genetic Regulatory Sequences, Nucleic Acid
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Deep learning of immune cell differentiation.

Proceedings of the National Academy of Sciences of the United States of America Sep 25, 2020
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...
Deep Learning Mice Transcription Factors Humans Animals Regulatory Sequences, Nucleic Acid Chromatin Databases, Genetic Gene Expression Regulation Hematopoiesis
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Frequency spectra characterization of noncoding human genomic sequences.

Genes & genomics Aug 31, 2020
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...
Genomics Nucleotides Genome, Human Humans Machine Learning Algorithms Regulatory Sequences, Nucleic Acid
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Predicting gene regulatory regions with a convolutional neural network for processing double-strand genome sequence information.

PloS one Jul 23, 2020
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...
Genomics Sequence Analysis, DNA DNA Genome Regulatory Sequences, Nucleic Acid Software Animals Mice Humans Neural Networks, Computer
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Large-scale DNA-based phenotypic recording and deep learning enable highly accurate sequence-function mapping.

Nature communications Jul 15, 2020
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...
Molecular Sequence Annotation Binding Sites Gene Knockout Techniques Genome, Bacterial Ribosomes Regulatory Sequences, Nucleic Acid Sequence Analysis, DNA High-Throughput Nucleotide Sequencing Datasets as Topic Deep Learning Computational Biology Escherichia coli Phenotype
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AIKYATAN: mapping distal regulatory elements using convolutional learning on GPU.

BMC bioinformatics Oct 7, 2019
BACKGROUND: The data deluge can leverage sophisticated ML techniques for functionally annotating the regulatory non-coding genome. The challenge lies in selecting the appropriate classifier for the specific functional annotation problem, within the b...
Humans Software Deep Learning Epigenomics Cell Line Chromosome Mapping Regulatory Sequences, Nucleic Acid
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Deep learning for DNase I hypersensitive sites identification.

BMC genomics Dec 31, 2018
BACKGROUND: The DNase I hypersensitive sites (DHSs) are associated with the cis-regulatory DNA elements. An efficient method of identifying DHSs can enhance the understanding on the accessibility of chromatin. Despite a multitude of resources availab...
Arabidopsis Regulatory Sequences, Nucleic Acid Humans Deoxyribonuclease I Deep Learning Computational Biology Oryza Models, Biological
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Genome-wide prediction of cis-regulatory regions using supervised deep learning methods.

BMC bioinformatics May 31, 2018
BACKGROUND: In the human genome, 98% of DNA sequences are non-protein-coding regions that were previously disregarded as junk DNA. In fact, non-coding regions host a variety of cis-regulatory regions which precisely control the expression of genes. T...
Humans Neural Networks, Computer Machine Learning Regulatory Sequences, Nucleic Acid Deep Learning Genomics Gene Expression Regulation
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Sequential regulatory activity prediction across chromosomes with convolutional neural networks.

Genome research Mar 27, 2018
Models for predicting phenotypic outcomes from genotypes have important applications to understanding genomic function and improving human health. Here, we develop a machine-learning system to predict cell-type-specific epigenetic and transcriptional...
Promoter Regions, Genetic Chromosomes Regulatory Sequences, Nucleic Acid Genomics Polymorphism, Single Nucleotide Computational Biology Epigenomics Models, Genetic Gene Expression Regulation Machine Learning Animals Gene Expression Profiling Humans Neural Networks, Computer
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