AIMC Topic: Gene Expression Regulation

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Advances in computational and experimental approaches for deciphering transcriptional regulatory networks: Understanding the roles of cis-regulatory elements is essential, and recent research utilizing MPRAs, STARR-seq, CRISPR-Cas9, and machine learning has yielded valuable insights.

BioEssays : news and reviews in molecular, cellular and developmental biology May 8, 2024
Understanding the influence of cis-regulatory elements on gene regulation poses numerous challenges given complexities stemming from variations in transcription factor (TF) binding, chromatin accessibility, structural constraints, and cell-type diffe...
Machine Learning Animals Gene Regulatory Networks Humans CRISPR-Cas Systems Gene Expression Regulation Regulatory Elements, Transcriptional Computational Biology Transcription Factors
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CoVar: A generalizable machine learning approach to identify the coordinated regulators driving variational gene expression.

PLoS computational biology Apr 17, 2024
Network inference is used to model transcriptional, signaling, and metabolic interactions among genes, proteins, and metabolites that identify biological pathways influencing disease pathogenesis. Advances in machine learning (ML)-based inference mod...
Saccharomyces cerevisiae Computer Simulation Gene Expression Profiling Computational Biology Gene Expression Regulation Gene Regulatory Networks Machine Learning Algorithms
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Cell-type-directed design of synthetic enhancers.

Nature Dec 12, 2023
Transcriptional enhancers act as docking stations for combinations of transcription factors and thereby regulate spatiotemporal activation of their target genes. It has been a long-standing goal in the field to decode the regulatory logic of an enhan...
Animals Deep Learning Transcription Factors Humans Brain Gene Expression Regulation Drosophila melanogaster Cells Animals, Genetically Modified Synthetic Biology Enhancer Elements, Genetic Repressor Proteins Neuroglia
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IChrom-Deep: An Attention-Based Deep Learning Model for Identifying Chromatin Interactions.

IEEE journal of biomedical and health informatics Sep 6, 2023
Identification of chromatin interactions is crucial for advancing our knowledge of gene regulation. However, due to the limitations of high-throughput experimental techniques, there is an urgent need to develop computational methods for predicting ch...
Humans Deep Learning Gene Expression Regulation Chromatin Genomics
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Interpretable single-cell transcription factor prediction based on deep learning with attention mechanism.

Computational biology and chemistry Aug 7, 2023
Predicting the transcription factor binding site (TFBS) in the whole genome range is essential in exploring the rule of gene transcription control. Although many deep learning methods to predict TFBS have been proposed, predicting TFBS using single-c...
Gene Expression Regulation Chromatin Deep Learning Transcription Factors Neural Networks, Computer
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Systematical analysis of underlying markers associated with Marfan syndrome via integrated bioinformatics and machine learning strategies.

Journal of biomolecular structure & dynamics Jul 14, 2023
Marfan syndrome (MFS) is a hereditary disease with high mortality. This study aimed to explore peripheral blood potential markers and underlying mechanisms in MFS via a series bioinformatics and machine learning analysis. First, we downloaded two MFS...
Humans Algorithms Databases, Genetic Biomarkers Protein Interaction Maps Machine Learning MicroRNAs Gene Expression Regulation Marfan Syndrome Gene Expression Profiling Gene Regulatory Networks ROC Curve Computational Biology
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A Unified Deep Learning Framework for Single-Cell ATAC-Seq Analysis Based on ProdDep Transformer Encoder.

International journal of molecular sciences Mar 1, 2023
Recent advances in single-cell sequencing assays for the transposase-accessibility chromatin (scATAC-seq) technique have provided cell-specific chromatin accessibility landscapes of cis-regulatory elements, providing deeper insights into cellular sta...
Chromatin Immunoprecipitation Sequencing Transcription Factors Gene Expression Regulation Chromatin Deep Learning Single-Cell Analysis
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A universal deep-learning model for zinc finger design enables transcription factor reprogramming.

Nature biotechnology Jan 26, 2023
CysHis zinc finger (ZF) domains engineered to bind specific target sequences in the genome provide an effective strategy for programmable regulation of gene expression, with many potential therapeutic applications. However, the structurally intricate...
Humans DNA Deep Learning Gene Expression Regulation Transcription Factors Zinc Fingers
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Ensemble learning based assessment of the role of transcription factors in gene expression.

Computers in biology and medicine Dec 21, 2022
Cancer cells are formed when the associated, active genes fail to function the way they are meant to function. Multiple genes collectively control cell growth by activating a proper set of genes. Regulation of gene expression is controlled through th...
Gene Expression Regulation Promoter Regions, Genetic Transcription Factors Gene Expression Machine Learning Cell Line, Tumor
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Deep learning of cross-species single-cell landscapes identifies conserved regulatory programs underlying cell types.

Nature genetics Oct 13, 2022
Despite extensive efforts to generate and analyze reference genomes, genetic models to predict gene regulation and cell fate decisions are lacking for most species. Here, we generated whole-body single-cell transcriptomic landscapes of zebrafish, Dro...
Gene Expression Regulation Transcription Factors Conserved Sequence Animals Drosophila Deep Learning Zebrafish
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