AIMC Topic: 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) Aug 2, 2024
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...
Software Algorithms Sequence Analysis, DNA DNA Binding Sites Deep Learning Computational Biology Transcription Factors
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Reinventing gene expression connectivity through regulatory and spatial structural empowerment via principal node aggregation graph neural network.

Nucleic acids research Jul 22, 2024
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...
Humans Neural Networks, Computer Algorithms Gene Expression Regulation Genome, Human Histone Code Protein Interaction Maps Gene Regulatory Networks Transcription Factors
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The developmental and evolutionary characteristics of transcription factor binding site clustered regions based on an explainable machine learning model.

Nucleic acids research Jul 22, 2024
Gene expression is temporally and spatially regulated by the interaction of transcription factors (TFs) and cis-regulatory elements (CREs). The uneven distribution of TF binding sites across the genome poses challenges in understanding how this distr...
Oryzias Gene Expression Regulation, Developmental Chromatin Animals Embryonic Development Humans Chickens Cattle Promoter Regions, Genetic Machine Learning Mice Binding Sites Transcription Factors Evolution, Molecular
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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) Jul 1, 2024
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...
Sequence Analysis, RNA Transcription Factors Gene Expression Profiling Transcriptome Deep Learning Single-Cell Analysis Gene Regulatory Networks Computational Biology Humans Neural Networks, Computer Algorithms
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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) Jun 3, 2024
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...
Enhancer Elements, Genetic Chromatin Immunoprecipitation Sequencing Humans Genomics Transcription Factors Supervised Machine Learning Promoter Regions, Genetic
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PTFSpot: deep co-learning on transcription factors and their binding regions attains impeccable universality in plants.

Briefings in bioinformatics May 23, 2024
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...
Plants Binding Sites Genome, Plant Arabidopsis Deep Learning Computational Biology Transcription Factors Software
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DeepGRNCS: deep learning-based framework for jointly inferring gene regulatory networks across cell subpopulations.

Briefings in bioinformatics May 23, 2024
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...
Humans Deep Learning Transcription Factors Gene Regulatory Networks Computational Biology Single-Cell Analysis Sequence Analysis, RNA RNA-Seq
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BERT-TFBS: a novel BERT-based model for predicting transcription factor binding sites by transfer learning.

Briefings in bioinformatics Mar 27, 2024
Transcription factors (TFs) are proteins essential for regulating genetic transcriptions by binding to transcription factor binding sites (TFBSs) in DNA sequences. Accurate predictions of TFBSs can contribute to the design and construction of metabol...
Humans Binding Sites Protein Binding Neural Networks, Computer Computational Biology Algorithms Transcription Factors Deep Learning
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Deep-Learning Uncovers certain CCM Isoforms as Transcription Factors.

Frontiers in bioscience (Landmark edition) Feb 21, 2024
BACKGROUND: Cerebral Cavernous Malformations (CCMs) are brain vascular abnormalities associated with an increased risk of hemorrhagic strokes. Familial CCMs result from autosomal dominant inheritance involving three genes: (), (), and (). CCM1 and...
Proto-Oncogene Proteins Humans Hemangioma, Cavernous, Central Nervous System Deep Learning Transcription Factors Protein Isoforms Carrier Proteins Receptors, Progesterone
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Deep Learning for Predicting Gene Regulatory Networks: A Step-by-Step Protocol in R.

Methods in molecular biology (Clifton, N.J.) Jan 1, 2024
Deep learning has emerged as a powerful tool for solving complex problems, including reconstruction of gene regulatory networks within the realm of biology. These networks consist of transcription factors and their associations with genes they regula...
Transcription Factors Algorithms Deep Learning Gene Regulatory Networks Humans Neural Networks, Computer
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