AIMC Topic: Gene Expression Regulation

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Sepsis Important Genes Identification Through Biologically Informed Deep Learning and Transcriptomic Analysis.

Clinical and experimental pharmacology & physiology Jul 1, 2025
Sepsis is a life-threatening disease caused by the dysregulation of the immune response. It is important to identify influential genes modulating the immune response in sepsis. In this study, we used P-NET, a biologically informed explainable artific...
Humans Gene Expression Profiling Signal Transduction Gene Expression Regulation Tissue Inhibitor of Metalloproteinase-1 Transcriptome Deep Learning Sepsis
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SCIG: Machine learning uncovers cell identity genes in single cells by genetic sequence codes.

Nucleic acids research May 22, 2025
Deciphering cell identity genes is pivotal to understanding cell differentiation, development, and cell identity dysregulation involving diseases. Here, we introduce SCIG, a machine-learning method to uncover cell identity genes in single cells. In a...
Humans Machine Learning Single-Cell Analysis Cell Differentiation Gene Expression Regulation Endothelial Cells Gene Regulatory Networks Epigenesis, Genetic Transcription Factors
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CREATE: cell-type-specific cis-regulatory element identification via discrete embedding.

Nature communications May 17, 2025
Cis-regulatory elements (CREs), including enhancers, silencers, promoters and insulators, play pivotal roles in orchestrating gene regulatory mechanisms that drive complex biological traits. However, current approaches for CRE identification are pred...
Enhancer Elements, Genetic Regulatory Sequences, Nucleic Acid Chromatin Humans Deep Learning Computational Biology Gene Expression Regulation Promoter Regions, Genetic
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Predicting gene expression from histone marks using chromatin deep learning models depends on histone mark function, regulatory distance and cellular states.

Nucleic acids research Feb 8, 2025
To understand the complex relationship between histone mark activity and gene expression, recent advances have used in silico predictions based on large-scale machine learning models. However, these approaches have omitted key contributing factors li...
Humans Deep Learning Histones Histone Code Gene Expression Regulation Transcription, Genetic Promoter Regions, Genetic Chromatin
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miRStart 2.0: enhancing miRNA regulatory insights through deep learning-based TSS identification.

Nucleic acids research Jan 6, 2025
MicroRNAs (miRNAs) are small non-coding RNAs that regulate gene expression by binding to the 3'-untranslated regions of target mRNAs, influencing various biological processes at the post-transcriptional level. Identifying miRNA transcription start si...
Transcription Factors Animals MicroRNAs Humans Gene Expression Regulation Deep Learning Transcription Initiation Site Software Mice
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Identification of regulatory cell death-related genes during MASH progression using bioinformatics analysis and machine learning strategies.

Frontiers in immunology Jan 1, 2025
BACKGROUND: Metabolic dysfunction-associated steatohepatitis (MASH) is becoming increasingly prevalent. Regulated cell death (RCD) has emerged as a significant disease phenotype and may act as a marker for liver fibrosis. The present study aimed to i...
Gene Ontology Non-alcoholic Fatty Liver Disease Transcriptome Cell Death Gene Expression Regulation Mice Animals Computational Biology Disease Progression Liver Cirrhosis Humans Gene Expression Profiling Databases, Genetic Machine Learning Disease Models, Animal
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A novel interpretable deep learning-based computational framework designed synthetic enhancers with broad cross-species activity.

Nucleic acids research Nov 27, 2024
Enhancers play a critical role in dynamically regulating spatial-temporal gene expression and establishing cell identity, underscoring the significance of designing them with specific properties for applications in biosynthetic engineering and gene t...
Animals Deep Learning Computational Biology Gene Expression Regulation Drosophila melanogaster Drosophila Enhancer Elements, Genetic Humans
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A self-attention-driven deep learning framework for inference of transcriptional gene regulatory networks.

Briefings in bioinformatics Nov 22, 2024
The interactions between transcription factors (TFs) and the target genes could provide a basis for constructing gene regulatory networks (GRNs) for mechanistic understanding of various biological complex processes. From gene expression data, particu...
Transcription Factors Gene Expression Regulation Gene Regulatory Networks Computational Biology Gene Expression Profiling Transcriptome Deep Learning Humans Software Algorithms
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A genome-scale deep learning model to predict gene expression changes of genetic perturbations from multiplex biological networks.

Briefings in bioinformatics Jul 25, 2024
Systematic characterization of biological effects to genetic perturbation is essential to the application of molecular biology and biomedicine. However, the experimental exhaustion of genetic perturbations on the genome-wide scale is challenging. Her...
Gene Expression Regulation Deep Learning Humans RNA Interference Gene Regulatory Networks Gene Expression Profiling Computational Biology
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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...
Gene Expression Regulation Genome, Human Histone Code Gene Regulatory Networks Humans Neural Networks, Computer Transcription Factors Algorithms Protein Interaction Maps
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