AIMC Topic: Transcription Factors

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A Unified Deep Learning Framework for Single-Cell ATAC-Seq Analysis Based on ProdDep Transformer Encoder.

International journal of molecular sciences Mar 1, 2023
Recent advances in single-cell sequencing assays for the transposase-accessibility chromatin (scATAC-seq) technique have provided cell-specific chromatin accessibility landscapes of cis-regulatory elements, providing deeper insights into cellular sta...
Deep Learning Single-Cell Analysis Chromatin Chromatin Immunoprecipitation Sequencing Transcription Factors Gene Expression Regulation
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Explainable artificial intelligence as a reliable annotator of archaeal promoter regions.

Scientific reports Jan 31, 2023
Archaea are a vast and unexplored cellular domain that thrive in a high diversity of environments, having central roles in processes mediating global carbon and nutrient fluxes. For these organisms to balance their metabolism, the appropriate regulat...
Artificial Intelligence Machine Learning Promoter Regions, Genetic Archaea Transcription Factors
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A universal deep-learning model for zinc finger design enables transcription factor reprogramming.

Nature biotechnology Jan 26, 2023
CysHis zinc finger (ZF) domains engineered to bind specific target sequences in the genome provide an effective strategy for programmable regulation of gene expression, with many potential therapeutic applications. However, the structurally intricate...
Gene Expression Regulation Humans Deep Learning Zinc Fingers Transcription Factors DNA
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Ensemble learning based assessment of the role of transcription factors in gene expression.

Computers in biology and medicine Dec 21, 2022
Cancer cells are formed when the associated, active genes fail to function the way they are meant to function. Multiple genes collectively control cell growth by activating a proper set of genes. Regulation of gene expression is controlled through th...
Transcription Factors Gene Expression Cell Line, Tumor Gene Expression Regulation Promoter Regions, Genetic Machine Learning
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Prediction of Transcription Factor Binding Sites With an Attention Augmented Convolutional Neural Network.

IEEE/ACM transactions on computational biology and bioinformatics Dec 8, 2022
Identification of transcription factor binding sites (TFBSs) is essential for revealing the rules of protein-DNA binding. Although some computational methods have been presented to predict TFBSs using epigenomic and sequence features, most of them ig...
Protein Binding Transcription Factors Binding Sites Neural Networks, Computer Algorithms
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Deep learning of cross-species single-cell landscapes identifies conserved regulatory programs underlying cell types.

Nature genetics Oct 13, 2022
Despite extensive efforts to generate and analyze reference genomes, genetic models to predict gene regulation and cell fate decisions are lacking for most species. Here, we generated whole-body single-cell transcriptomic landscapes of zebrafish, Dro...
Animals Deep Learning Transcription Factors Drosophila Zebrafish Conserved Sequence Gene Expression Regulation
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DLoopCaller: A deep learning approach for predicting genome-wide chromatin loops by integrating accessible chromatin landscapes.

PLoS computational biology Oct 7, 2022
In recent years, major advances have been made in various chromosome conformation capture technologies to further satisfy the needs of researchers for high-quality, high-resolution contact interactions. Discriminating the loops from genome-wide conta...
Chromatin Deep Learning Transcription Factors Genome Chromosomes
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TAMC: A deep-learning approach to predict motif-centric transcriptional factor binding activity based on ATAC-seq profile.

PLoS computational biology Sep 12, 2022
Determining transcriptional factor binding sites (TFBSs) is critical for understanding the molecular mechanisms regulating gene expression in different biological conditions. Biological assays designed to directly mapping TFBSs require large sample s...
Chromatin Immunoprecipitation Sequencing Deep Learning Transcription Factors Binding Sites Protein Binding
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Multinomial Convolutions for Joint Modeling of Regulatory Motifs and Sequence Activity Readouts.

Genes Sep 8, 2022
A common goal in the convolutional neural network (CNN) modeling of genomic data is to discover specific sequence motifs. Post hoc analysis methods aid in this task but are dependent on parameters whose optimal values are unclear and applying the dis...
Transcription Factors Neural Networks, Computer Genomics
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Nested epistasis enhancer networks for robust genome regulation.

Science (New York, N.Y.) Aug 11, 2022
Mammalian genomes have multiple enhancers spanning an ultralong distance (>megabases) to modulate important genes, but it is unclear how these enhancers coordinate to achieve this task. We combine multiplexed CRISPRi screening with machine learning t...
Nuclear Proteins Epistasis, Genetic Machine Learning Cell Cycle Proteins Transcription Factors Enhancer Elements, Genetic Disease Humans Genome-Wide Association Study K562 Cells
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