AIMC Topic: Transcription Factors

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DeepSTF: predicting transcription factor binding sites by interpretable deep neural networks combining sequence and shape.

Briefings in bioinformatics Jul 20, 2023
Precise targeting of transcription factor binding sites (TFBSs) is essential to comprehending transcriptional regulatory processes and investigating cellular function. Although several deep learning algorithms have been created to predict TFBSs, the ...
Transcription Factors DNA Binding Sites Neural Networks, Computer Protein Binding
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Cooperation of local features and global representations by a dual-branch network for transcription factor binding sites prediction.

Briefings in bioinformatics Mar 19, 2023
Interactions between DNA and transcription factors (TFs) play an essential role in understanding transcriptional regulation mechanisms and gene expression. Due to the large accumulation of training data and low expense, deep learning methods have sho...
DNA Binding Sites Protein Binding Transcription Factors Neural Networks, Computer
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Interpretable deep learning for chromatin-informed inference of transcriptional programs driven by somatic alterations across cancers.

Nucleic acids research Oct 28, 2022
Cancer is a disease of gene dysregulation, where cells acquire somatic and epigenetic alterations that drive aberrant cellular signaling. These alterations adversely impact transcriptional programs and cause profound changes in gene expression. Inter...
Neoplasms Humans Deep Learning Precision Medicine Transcription Factors Chromatin
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DNAffinity: a machine-learning approach to predict DNA binding affinities of transcription factors.

Nucleic acids research Sep 9, 2022
We present a physics-based machine learning approach to predict in vitro transcription factor binding affinities from structural and mechanical DNA properties directly derived from atomistic molecular dynamics simulations. The method is able to predi...
Transcription Factors DNA Algorithms Protein Binding Binding Sites Machine Learning
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Identifying modifications on DNA-bound histones with joint deep learning of multiple binding sites in DNA sequence.

Bioinformatics (Oxford, England) Sep 2, 2022
MOTIVATION: Histone modifications are epigenetic markers that impact gene expression by altering the chromatin structure or recruiting histone modifiers. Their accurate identification is key to unraveling the mechanisms by which they regulate gene ex...
Transcription Factors DNA Base Sequence Deep Learning Histones Binding Sites
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Genome-wide cis-decoding for expression design in tomato using cistrome data and explainable deep learning.

The Plant cell May 24, 2022
In the evolutionary history of plants, variation in cis-regulatory elements (CREs) resulting in diversification of gene expression has played a central role in driving the evolution of lineage-specific traits. However, it is difficult to predict expr...
Plant Proteins Gene Expression Regulation, Plant Regulatory Sequences, Nucleic Acid Solanum lycopersicum Fruit Deep Learning Transcription Factors
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Machine Learning Prediction of Non-Coding Variant Impact in Human Retinal cis-Regulatory Elements.

Translational vision science & technology Apr 1, 2022
PURPOSE: Prior studies have demonstrated the significance of specific cis-regulatory variants in retinal disease; however, determining the functional impact of regulatory variants remains a major challenge. In this study, we utilized a machine learni...
Phylogeny Transcription Factors Nucleotides Retinal Diseases Humans Retina Machine Learning
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Detection of transcription factors binding to methylated DNA by deep recurrent neural network.

Briefings in bioinformatics Jan 17, 2022
Transcription factors (TFs) are proteins specifically involved in gene expression regulation. It is generally accepted in epigenetics that methylated nucleotides could prevent the TFs from binding to DNA fragments. However, recent studies have confir...
Binding Sites Protein Binding DNA-Binding Proteins Algorithms DNA Methylation Gene Expression Regulation Humans Transcription Factors DNA Deep Learning Neural Networks, Computer
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SIGNET: single-cell RNA-seq-based gene regulatory network prediction using multiple-layer perceptron bagging.

Briefings in bioinformatics Jan 17, 2022
High-throughput single-cell RNA-seq data have provided unprecedented opportunities for deciphering the regulatory interactions among genes. However, such interactions are complex and often nonlinear or nonmonotonic, which makes their inference using ...
Humans Cluster Analysis Gene Expression Profiling Gene Expression Regulation Neural Networks, Computer Deep Learning Sequence Analysis, RNA Single-Cell Analysis RNA-Seq Algorithms Transcription Factors Gene Regulatory Networks
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A deep learning model to identify gene expression level using cobinding transcription factor signals.

Briefings in bioinformatics Jan 17, 2022
Gene expression is directly controlled by transcription factors (TFs) in a complex combination manner. It remains a challenging task to systematically infer how the cooperative binding of TFs drives gene activity. Here, we quantitatively analyzed the...
Gene Regulatory Networks Transcription Factors Gene Expression Deep Learning Gene Expression Regulation
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