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Transcription Factors

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PTFSpot: deep co-learning on transcription factors and their binding regions attains impeccable universality in plants.

Briefings in bioinformatics 39013383
Unlike animals, variability in transcription factors (TFs) and their binding regions (TFBRs) across the plants species is a major problem that most of the existing TFBR finding software fail to tackle, rendering them hardly of any use. This limitatio...
Arabidopsis Binding Sites Computational Biology Deep Learning Genome, Plant Plants Software Transcription Factors
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DeepGRNCS: deep learning-based framework for jointly inferring gene regulatory networks across cell subpopulations.

Briefings in bioinformatics 38980373
Inferring gene regulatory networks (GRNs) allows us to obtain a deeper understanding of cellular function and disease pathogenesis. Recent advances in single-cell RNA sequencing (scRNA-seq) technology have improved the accuracy of GRN inference. Howe...
Computational Biology Deep Learning Gene Regulatory Networks Humans RNA-Seq Sequence Analysis, RNA Single-Cell Analysis Transcription Factors
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Supervised learning of enhancer-promoter specificity based on genome-wide perturbation studies highlights areas for improvement in learning.

Bioinformatics (Oxford, England) 38870532
MOTIVATION: Understanding the rules that govern enhancer-driven transcription remains a central unsolved problem in genomics. Now with multiple massively parallel enhancer perturbation assays published, there are enough data that we can utilize to le...
Chromatin Immunoprecipitation Sequencing Enhancer Elements, Genetic Genomics Humans Promoter Regions, Genetic Supervised Machine Learning Transcription Factors
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AI-Assisted Rational Design and Activity Prediction of Biological Elements for Optimizing Transcription-Factor-Based Biosensors.

Molecules (Basel, Switzerland) 39124917
The rational design, activity prediction, and adaptive application of biological elements (bio-elements) are crucial research fields in synthetic biology. Currently, a major challenge in the field is efficiently designing desired bio-elements and acc...
Algorithms Artificial Intelligence Biosensing Techniques Deep Learning Humans Machine Learning Synthetic Biology Transcription Factors
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Cross-Species Prediction of Transcription Factor Binding by Adversarial Training of a Novel Nucleotide-Level Deep Neural Network.

Advanced science (Weinheim, Baden-Wurttemberg, Germany) 39076052
Cross-species prediction of TF binding remains a major challenge due to the rapid evolutionary turnover of individual TF binding sites, resulting in cross-species predictive performance being consistently worse than within-species performance. In thi...
Animals Binding Sites Computational Biology Deep Learning Humans Mice Neural Networks, Computer Nucleotides Polymorphism, Single Nucleotide Species Specificity Transcription Factors
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TFscope: systematic analysis of the sequence features involved in the binding preferences of transcription factors.

Genome biology 38987807
Characterizing the binding preferences of transcription factors (TFs) in different cell types and conditions is key to understand how they orchestrate gene expression. Here, we develop TFscope, a machine learning approach that identifies sequence fea...
Binding Sites Chromatin Immunoprecipitation Sequencing Humans Machine Learning Nucleotide Motifs Protein Binding Transcription Factors
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SFINN: inferring gene regulatory network from single-cell and spatial transcriptomic data with shared factor neighborhood and integrated neural network.

Bioinformatics (Oxford, England) 38950180
MOTIVATION: The rise of single-cell RNA sequencing (scRNA-seq) technology presents new opportunities for constructing detailed cell type-specific gene regulatory networks (GRNs) to study cell heterogeneity. However, challenges caused by noises, techn...
Algorithms Computational Biology Deep Learning Gene Expression Profiling Gene Regulatory Networks Humans Neural Networks, Computer Sequence Analysis, RNA Single-Cell Analysis Transcription Factors Transcriptome
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Reinventing gene expression connectivity through regulatory and spatial structural empowerment via principal node aggregation graph neural network.

Nucleic acids research 38884259
The intricacies of the human genome, manifested as a complex network of genes, transcend conventional representations in text or numerical matrices. The intricate gene-to-gene relationships inherent in this complexity find a more suitable depiction i...
Algorithms Gene Expression Regulation Gene Regulatory Networks Genome, Human Histone Code Humans Neural Networks, Computer Protein Interaction Maps Transcription Factors
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BertSNR: an interpretable deep learning framework for single-nucleotide resolution identification of transcription factor binding sites based on DNA language model.

Bioinformatics (Oxford, England) 39107889
MOTIVATION: Transcription factors are pivotal in the regulation of gene expression, and accurate identification of transcription factor binding sites (TFBSs) at high resolution is crucial for understanding the mechanisms underlying gene regulation. T...
Algorithms Binding Sites Computational Biology Deep Learning DNA Sequence Analysis, DNA Software Transcription Factors
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An integrated machine-learning model to predict nucleosome architecture.

Nucleic acids research 39162225
We demonstrate that nucleosomes placed in the gene body can be accurately located from signal decay theory assuming two emitters located at the beginning and at the end of genes. These generated wave signals can be in phase (leading to well defined n...
Binding Sites DNA Humans Machine Learning Nucleosomes Transcription Factors
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