AIMC Topic: Transcription Factors

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Predictive biophysical neural network modeling of a compendium of in vivo transcription factor DNA binding profiles for Escherichia coli.

Nature communications May 7, 2025
The DNA binding of most Escherichia coli Transcription Factors (TFs) has not been comprehensively mapped, and few have models that can quantitatively predict binding affinity. We report the global mapping of in vivo DNA binding for 139 E. coli TFs us...
Neural Networks, Computer Protein Binding DNA, Bacterial Binding Sites Chromatin Immunoprecipitation Sequencing Transcription Factors Escherichia coli Escherichia coli Proteins
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A KAN-based hybrid deep neural networks for accurate identification of transcription factor binding sites.

PloS one May 7, 2025
BACKGROUND: Predicting protein-DNA binding sites in vivo is a challenging but urgent task in many fields such as drug design and development. Most promoters contain many transcription factor (TF) binding sites, yet only a few have been identified thr...
Transcription Factors Deep Learning Humans Promoter Regions, Genetic Protein Binding Neural Networks, Computer Binding Sites Computational Biology Algorithms
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Integrating genetic variation with deep learning provides context for variants impacting transcription factor binding during embryogenesis.

Genome research May 2, 2025
Understanding how genetic variation impacts transcription factor (TF) binding remains a major challenge, limiting our ability to model disease-associated variants. Here, we used a highly controlled system of F crosses with extensive genetic diversity...
Drosophila melanogaster Binding Sites Drosophila Deep Learning Embryonic Development Animals Alleles Protein Binding Drosophila Proteins Genetic Variation Transcription Factors
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Neural network conditioned to produce thermophilic protein sequences can increase thermal stability.

Scientific reports Apr 23, 2025
This work presents Neural Optimization for Melting-temperature Enabled by Leveraging Translation (NOMELT), a novel approach for designing and ranking high-temperature stable proteins using neural machine translation. The model, trained on over 4 mill...
Amino Acid Sequence Protein Stability Drosophila Proteins Drosophila melanogaster Neural Networks, Computer Animals Homeodomain Proteins Temperature Transcription Factors
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TRAPT: a multi-stage fused deep learning framework for predicting transcriptional regulators based on large-scale epigenomic data.

Nature communications Apr 16, 2025
It is challenging to identify regulatory transcriptional regulators (TRs), which control gene expression via regulatory elements and epigenomic signals, in context-specific studies on the onset and progression of diseases. The use of large-scale mult...
Chromatin Gene Expression Regulation Binding Sites Computational Biology Regulatory Elements, Transcriptional Epigenomics Transcription Factors Humans Deep Learning
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Interpreting regulatory mechanisms of Hippo signaling through a deep learning sequence model.

Cell genomics Apr 1, 2025
Signaling pathway components are well studied, but how they mediate cell-type-specific transcription responses is an unresolved problem. Using the Hippo pathway in mouse trophoblast stem cells as a model, we show that the DNA binding of signaling eff...
Molecular Dynamics Simulation Phosphoproteins Trophoblasts Enhancer Elements, Genetic Protein Serine-Threonine Kinases Adaptor Proteins, Signal Transducing DNA-Binding Proteins TEA Domain Transcription Factors Muscle Proteins Hippo Signaling Pathway YAP-Signaling Proteins Animals Deep Learning Signal Transduction Transcription Factors Protein Binding Mice
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Deep Neural Network-Mining of Rice Drought-Responsive TF-TAG Modules by a Combinatorial Analysis of ATAC-Seq and RNA-Seq.

Plant, cell & environment Mar 31, 2025
Drought is a critical risk factor that impacts rice growth and yields. Previous studies have focused on the regulatory roles of individual transcription factors in response to drought stress. However, there is limited understanding of multi-factor st...
Neural Networks, Computer RNA-Seq Gene Regulatory Networks Transcription Factors Stress, Physiological Oryza Droughts Data Mining Chromatin Immunoprecipitation Sequencing Plant Proteins Gene Expression Regulation, Plant
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CacPred: a cascaded convolutional neural network for TF-DNA binding prediction.

BMC genomics Mar 18, 2025
BACKGROUND: Transcription factors (TFs) regulate the genes' expression by binding to DNA sequences. Aligned TFBSs of the same TF are seen as cis-regulatory motifs, and substantial computational efforts have been invested to find motifs. In recent yea...
Neural Networks, Computer Software Algorithms Computational Biology Transcription Factors Convolutional Neural Networks Protein Binding DNA Binding Sites Chromatin Immunoprecipitation Sequencing
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Transfer learning reveals sequence determinants of the quantitative response to transcription factor dosage.

Cell genomics Feb 27, 2025
Deep learning models have advanced our ability to predict cell-type-specific chromatin patterns from transcription factor (TF) binding motifs, but their application to perturbed contexts remains limited. We applied transfer learning to predict how co...
Chromatin Nucleosomes SOX9 Transcription Factor Deep Learning Humans Mice Protein Binding Animals Transcription Factors Binding Sites
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Identification of Biomarkers for Response to Interferon in Chronic Hepatitis B Based on Bioinformatics Analysis and Machine Learning.

Viral immunology Feb 24, 2025
Interferon (IFN) is a pivotal agent against hepatitis B virus (HBV) in clinic, but there is a lack of accurate biomarkers to predict the response to IFN therapy in patients with chronic hepatitis B (CHB). Our study aimed to investigate potential targ...
Humans Antiviral Agents MicroRNAs Machine Learning Computational Biology Hepatitis B virus Hepatitis B, Chronic Biomarkers Protein Interaction Maps RNA, Messenger Interferons Gene Expression Profiling Gene Regulatory Networks Transcription Factors
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