AIMC Topic: Transcription Factors

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

Analytical biochemistry Jan 31, 2021
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...
Databases, Genetic Enhancer Elements, Genetic Computational Biology Models, Genetic Transcription Factors Humans Neural Networks, Computer
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Machine learning approaches reveal subtle differences in breathing and sleep fragmentation in -derived astrocytes ablated mice.

Journal of neurophysiology Jan 27, 2021
Modern neurophysiology research requires the interrogation of high-dimensionality data sets. Machine learning and artificial intelligence (ML/AI) workflows have permeated into nearly all aspects of daily life in the developed world but have not been ...
Homeodomain Proteins Plethysmography Neurophysiology Genotype Animals Sleep Transcription Factors Electroencephalography Respiration Gene Expression Profiling Astrocytes Machine Learning Workflow Mice
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Systematic analysis of binding of transcription factors to noncoding variants.

Nature Jan 27, 2021
Many sequence variants have been linked to complex human traits and diseases, but deciphering their biological functions remains challenging, as most of them reside in noncoding DNA. Here we have systematically assessed the binding of 270 human trans...
Humans Support Vector Machine Ligands Genome, Human Disease Protein Binding Polymorphism, Single Nucleotide Transcription Factors Binding Sites SELEX Aptamer Technique
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An interpretable bimodal neural network characterizes the sequence and preexisting chromatin predictors of induced transcription factor binding.

Genome biology Jan 7, 2021
BACKGROUND: Transcription factor (TF) binding specificity is determined via a complex interplay between the transcription factor's DNA binding preference and cell type-specific chromatin environments. The chromatin features that correlate with transc...
Humans Neural Networks, Computer Protein Binding Transcription Factors Histones Binding Sites Genome Chromatin Basic Helix-Loop-Helix Transcription Factors DNA-Binding Proteins Gene Expression Regulation
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Global gene network exploration based on explainable artificial intelligence approach.

PloS one Nov 6, 2020
In recent years, personalized gene regulatory networks have received significant attention, and interpretation of the multilayer networks has been a critical issue for a comprehensive understanding of gene regulatory systems. Although several statist...
Principal Component Analysis Humans Epithelial-Mesenchymal Transition Biomarkers Artificial Intelligence Gene Regulatory Networks Software Transcription Factors
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Learning and interpreting the gene regulatory grammar in a deep learning framework.

PLoS computational biology Nov 2, 2020
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...
Databases, Genetic Terminology as Topic Binding Sites Enhancer Elements, Genetic Genes, Regulator Computational Biology Models, Genetic Transcription Factors Deep Learning Computer Simulation Gene Regulatory Networks Animals Mice Neural Networks, Computer
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A deep learning approach to programmable RNA switches.

Nature communications Oct 7, 2020
Engineered RNA elements are programmable tools capable of detecting small molecules, proteins, and nucleic acids. Predicting the behavior of these synthetic biology components remains a challenge, a situation that could be addressed through enhanced ...
Genome, Viral Thermodynamics Kinetics Genetic Engineering Datasets as Topic Humans Riboswitch Synthetic Biology Deep Learning Transcription Factors
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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...
Humans Regulatory Sequences, Nucleic Acid Chromatin Transcription Factors Animals Gene Expression Regulation Databases, Genetic Deep Learning Hematopoiesis Mice
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Transcription factor expression as a predictor of colon cancer prognosis: a machine learning practice.

BMC medical genomics Sep 21, 2020
BACKGROUND: Colon cancer is one of the leading causes of cancer deaths in the USA and around the world. Molecular level characters, such as gene expression levels and mutations, may provide profound information for precision treatment apart from path...
Biomarkers, Tumor Male Gene Expression Regulation, Neoplastic Proportional Hazards Models Kaplan-Meier Estimate Transcription Factors Machine Learning Colonic Neoplasms Humans Prognosis Female
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Enhancing the interpretability of transcription factor binding site prediction using attention mechanism.

Scientific reports Aug 7, 2020
Transcription factors (TFs) regulate the gene expression of their target genes by binding to the regulatory sequences of target genes (e.g., promoters and enhancers). To fully understand gene regulatory mechanisms, it is crucial to decipher the relat...
Base Sequence Binding Sites Neural Networks, Computer Models, Theoretical Protein Binding Transcription Factors Forecasting Computational Biology Gene Expression Regulation Deep Learning
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