AIMC Topic: Transcription Factors

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DeepD2V: A Novel Deep Learning-Based Framework for Predicting Transcription Factor Binding Sites from Combined DNA Sequence.

International journal of molecular sciences May 24, 2021
Predicting in vivo protein-DNA binding sites is a challenging but pressing task in a variety of fields like drug design and development. Most promoters contain a number of transcription factor (TF) binding sites, but only a small minority has been id...
Deep Learning Computational Biology Transcription Factors Humans Software Promoter Regions, Genetic K562 Cells Binding Sites Cell Line DNA DNA-Binding Proteins
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Machine learning predicts nucleosome binding modes of transcription factors.

BMC bioinformatics Mar 30, 2021
BACKGROUND: Most transcription factors (TFs) compete with nucleosomes to gain access to their cognate binding sites. Recent studies have identified several TF-nucleosome interaction modes including end binding (EB), oriented binding, periodic binding...
Protein Binding Transcription Factors Amino Acid Sequence Machine Learning Binding Sites Nucleosomes
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Epigenetic Target Fishing with Accurate Machine Learning Models.

Journal of medicinal chemistry Mar 26, 2021
Epigenetic targets are of significant importance in drug discovery research, as demonstrated by the eight approved epigenetic drugs for treatment of cancer and the increasing availability of chemogenomic data related to epigenetics. This data represe...
Histone Deacetylases Epigenomics Transcription Factors Structure-Activity Relationship Molecular Structure Databases, Chemical Proof of Concept Study Machine Learning Drug Discovery Organic Chemicals
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An Integrative Framework for Combining Sequence and Epigenomic Data to Predict Transcription Factor Binding Sites Using Deep Learning.

IEEE/ACM transactions on computational biology and bioinformatics Feb 3, 2021
Knowing the transcription factor binding sites (TFBSs) is essential for modeling the underlying binding mechanisms and follow-up cellular functions. Convolutional neural networks (CNNs) have outperformed methods in predicting TFBSs from the primary D...
Histone Code Chromatin Immunoprecipitation Sequencing Deep Learning Protein Binding Computational Biology Epigenesis, Genetic Transcription Factors Binding Sites
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DeepGRN: prediction of transcription factor binding site across cell-types using attention-based deep neural networks.

BMC bioinformatics Feb 1, 2021
BACKGROUND: Due to the complexity of the biological systems, the prediction of the potential DNA binding sites for transcription factors remains a difficult problem in computational biology. Genomic DNA sequences and experimental results from paralle...
Protein Binding Computational Biology Transcription Factors Neural Networks, Computer Binding Sites Chromatin
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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 Models, Genetic Transcription Factors Humans Neural Networks, Computer Computational Biology
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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 ...
Gene Expression Profiling Mice Genotype Transcription Factors Astrocytes Electroencephalography Machine Learning Animals Homeodomain Proteins Plethysmography Neurophysiology Workflow Sleep Respiration
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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...
SELEX Aptamer Technique Disease Binding Sites Humans Support Vector Machine Ligands Genome, Human Protein Binding Polymorphism, Single Nucleotide Transcription Factors
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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...
Binding Sites Genome Chromatin Gene Expression Regulation Histones Humans Neural Networks, Computer Protein Binding Transcription Factors Basic Helix-Loop-Helix Transcription Factors DNA-Binding Proteins
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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...
Transcription Factors Principal Component Analysis Epithelial-Mesenchymal Transition Software Biomarkers Gene Regulatory Networks Humans Artificial Intelligence
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