AIMC Topic: Transcription Factors

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Cell-type-directed design of synthetic enhancers.

Nature Dec 12, 2023
Transcriptional enhancers act as docking stations for combinations of transcription factors and thereby regulate spatiotemporal activation of their target genes. It has been a long-standing goal in the field to decode the regulatory logic of an enhan...
Synthetic Biology Gene Expression Regulation Transcription Factors Animals, Genetically Modified Repressor Proteins Neuroglia Enhancer Elements, Genetic Humans Drosophila melanogaster Animals Cells Brain Deep Learning
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FUN-PROSE: A deep learning approach to predict condition-specific gene expression in fungi.

PLoS computational biology Nov 16, 2023
mRNA levels of all genes in a genome is a critical piece of information defining the overall state of the cell in a given environmental condition. Being able to reconstruct such condition-specific expression in fungal genomes is particularly importan...
Saccharomyces cerevisiae Deep Learning Computational Biology Transcription Factors Gene Expression
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Improving Enhancer Identification with a Multi-Classifier Stacked Ensemble Model.

Journal of molecular biology Oct 16, 2023
Enhancers are DNA regions that are responsible for controlling the expression of genes. Enhancers are usually found upstream or downstream of a gene, or even inside a gene's intron region, but are normally located at a distant location from the genes...
Machine Learning Sequence Analysis, DNA DNA Enhancer Elements, Genetic Computational Biology Transcription Factors
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KDeep: a new memory-efficient data extraction method for accurately predicting DNA/RNA transcription factor binding sites.

Journal of translational medicine Oct 16, 2023
This paper addresses the crucial task of identifying DNA/RNA binding sites, which has implications in drug/vaccine design, protein engineering, and cancer research. Existing methods utilize complex neural network structures, diverse input types, and ...
Binding Sites Transcription Factors DNA Neural Networks, Computer Protein Binding
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DeepCAC: a deep learning approach on DNA transcription factors classification based on multi-head self-attention and concatenate convolutional neural network.

BMC bioinformatics Sep 18, 2023
Understanding gene expression processes necessitates the accurate classification and identification of transcription factors, which is supported by high-throughput sequencing technologies. However, these techniques suffer from inherent limitations su...
Computational Biology Transcription Factors Neural Networks, Computer Deep Learning DNA
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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...
Gene Expression Regulation Chromatin Neural Networks, Computer Deep Learning Transcription Factors
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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...
Biological Variation, Population Matrix Attachment Region Binding Proteins Machine Learning Algorithms Transcription Factors Phenotype Humans DNA-Binding Proteins Artificial Intelligence
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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 ...
DNA Chromatin Animals Chickens Swine Transcription Factors Deep Learning Cattle Humans Mice
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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...
Transcription Factors DNA Deep Learning Protein Binding Binding Sites
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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...
Virulence Transcription Factors Nucleotides Humans Binding Sites DNA Deep Learning Chromatin Immunoprecipitation Sequencing
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