AIMC Topic: Transcription Factors
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Dissecting crosstalk induced by cell-cell communication using single-cell transcriptomic data.
Nature communications
Jul 1, 2025
During cell-cell communication (CCC), pathways activated by different ligand-receptor pairs may have crosstalk with each other. While multiple methods have been developed to infer CCC networks and their downstream response using single-cell RNA-seq d...
Divergence in a eukaryotic transcription factor's co-TF dependence involves multiple intrinsically disordered regions.
Nature communications
Jun 18, 2025
Combinatorial control by transcription factors (TFs) is central to eukaryotic gene regulation, yet its mechanism, evolution, and regulatory impact are not well understood. Here we use natural variation in the yeast phosphate starvation (PHO) response...
Combinatorial discovery of microtopographical landscapes that resist biofilm formation through quorum sensing mediated autolubrication.
Nature communications
Jun 18, 2025
Bio-instructive materials that intrinsically inhibit biofilm formation have significant anti-biofouling potential in industrial and healthcare settings. Since bacterial surface attachment is sensitive to surface topography, we experimentally surveyed...
A library of lineage-specific driver lines connects developing neuronal circuits to behavior in the ventral nerve cord.
eLife
Jun 10, 2025
Understanding developmental changes in neuronal lineages is crucial to elucidate how they assemble into functional neural networks. Studies investigating nervous system development in model systems have only focused on select regions of the CNS due t...
Machine learning Reveals ATM and CNOT6L as critical factors in Cataract pathogenesis.
Experimental eye research
May 23, 2025
OBJECTIVE: Cataract, a common age-related blinding eye disease, has a complex pathogenesis. This study aims to identify key genes and potential mechanisms associated with cataracts, offering new targets and insights for its prevention and treatment.
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...
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...
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...
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...
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...
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