AIMC Topic: Transcription Factors

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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...
Conserved Sequence Gene Expression Regulation Drosophila Animals Deep Learning Transcription Factors Zebrafish
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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...
Deep Learning Chromosomes Chromatin Transcription Factors Genome
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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...
Deep Learning Binding Sites Chromatin Immunoprecipitation Sequencing Protein Binding Transcription Factors
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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...
Epistasis, Genetic Enhancer Elements, Genetic K562 Cells Cell Cycle Proteins Genome-Wide Association Study Nuclear Proteins Transcription Factors Disease Humans Machine Learning
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bHLHDB: A next generation database of basic helix loop helix transcription factors based on deep learning model.

Journal of bioinformatics and computational biology Jul 25, 2022
The basic helix loop helix (bHLH) superfamily is a large and diverse protein family that plays a role in various vital functions in nearly all animals and plants. The bHLH proteins form one of the largest families of transcription factors found in pl...
Plants Amino Acid Sequence Phylogeny Deep Learning Transcription Factors Basic Helix-Loop-Helix Transcription Factors Animals
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Deep learning-based transcription factor activity for stratification of breast cancer patients.

Biochimica et biophysica acta. Gene regulatory mechanisms Jun 8, 2022
Transcription factors directly bind to DNA and regulate the expression of the gene, causing epigenetic modification of the DNA. They often mediate epigenetic parameters of transcriptional and posttranscriptional mechanisms, and their expression activ...
Transcription Factors Deep Learning Genomics Female Breast Neoplasms Humans Algorithms
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Characterizing collaborative transcription regulation with a graph-based deep learning approach.

PLoS computational biology Jun 6, 2022
Human epigenome and transcription activities have been characterized by a number of sequence-based deep learning approaches which only utilize the DNA sequences. However, transcription factors interact with each other, and their collaborative regulat...
Deep Learning Protein Binding Transcription Factors DNA Humans Gene Expression Regulation Chromatin Binding Sites
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Prediction of protein-ligand binding affinity from sequencing data with interpretable machine learning.

Nature biotechnology May 23, 2022
Protein-ligand interactions are increasingly profiled at high throughput using affinity selection and massively parallel sequencing. However, these assays do not provide the biophysical parameters that most rigorously quantify molecular interactions....
Binding Sites Chromatin Immunoprecipitation DNA Ligands Machine Learning Protein Binding Transcription Factors
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Prediction of the transcription factor binding sites with meta-learning.

Methods (San Diego, Calif.) Apr 21, 2022
With the accumulation of ChIP-seq data, convolution neural network (CNN)-based methods have been proposed for predicting transcription factor binding sites (TFBSs). However, biological experimental data are noisy, and are often treated as ground trut...
Chromatin Immunoprecipitation Sequencing Transcription Factors Neural Networks, Computer Binding Sites Protein Binding
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