AIMC Topic: Transcription Factors

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Interpretable single-cell transcription factor prediction based on deep learning with attention mechanism.

Computational biology and chemistry Aug 7, 2023
Predicting the transcription factor binding site (TFBS) in the whole genome range is essential in exploring the rule of gene transcription control. Although many deep learning methods to predict TFBS have been proposed, predicting TFBS using single-c...
Deep Learning Transcription Factors Gene Expression Regulation Chromatin Neural Networks, Computer
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PhenoScore quantifies phenotypic variation for rare genetic diseases by combining facial analysis with other clinical features using a machine-learning framework.

Nature genetics Aug 7, 2023
Several molecular and phenotypic algorithms exist that establish genotype-phenotype correlations, including facial recognition tools. However, no unified framework that investigates both facial data and other phenotypic data directly from individuals...
Machine Learning Algorithms Humans Artificial Intelligence DNA-Binding Proteins Biological Variation, Population Matrix Attachment Region Binding Proteins Phenotype Transcription Factors
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DeepSATA: A Deep Learning-Based Sequence Analyzer Incorporating the Transcription Factor Binding Affinity to Dissect the Effects of Non-Coding Genetic Variants.

International journal of molecular sciences Jul 27, 2023
Utilizing large-scale epigenomics data, deep learning tools can predict the regulatory activity of genomic sequences, annotate non-coding genetic variants, and uncover mechanisms behind complex traits. However, these tools primarily rely on human or ...
Animals Swine Humans Mice Chickens Deep Learning Cattle Chromatin Transcription Factors DNA
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Modulation of DNA-protein Interactions by Proximal Genetic Elements as Uncovered by Interpretable Deep Learning.

Journal of molecular biology Apr 24, 2023
Transcription factors (TF) recognize specific motifs in the genome that are typically 6-12 bp long to regulate various aspects of the cellular machinery. Presence of binding motifs and favorable genome accessibility are key drivers for a consistent T...
Binding Sites Deep Learning Protein Binding Transcription Factors DNA
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How Deepbics Quantifies Intensities of Transcription Factor-DNA Binding and Facilitates Prediction of Single Nucleotide Variant Pathogenicity With a Deep Learning Model Trained On ChIP-Seq Data Sets.

IEEE/ACM transactions on computational biology and bioinformatics Apr 3, 2023
The binding of DNA sequences to cell type-specific transcription factors is essential for regulating gene expression in all organisms. Many variants occurring in these binding regions play crucial roles in human disease by disrupting the cis-regulati...
Binding Sites Chromatin Immunoprecipitation Sequencing Virulence Nucleotides Humans Transcription Factors Deep Learning DNA
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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 Promoter Regions, Genetic Archaea Machine Learning 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 Zinc Fingers Transcription Factors DNA Humans Deep Learning
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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...
Promoter Regions, Genetic Cell Line, Tumor Gene Expression Machine Learning Transcription Factors Gene Expression Regulation
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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...
Binding Sites Protein Binding Neural Networks, Computer Transcription Factors Algorithms
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