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Neural Regulation01:37

Neural Regulation

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Digestion begins with a cephalic phase that prepares the digestive system to receive food. When our brain processes visual or olfactory information about food, it triggers impulses in the cranial nerves innervating the salivary glands and stomach to prepare for food.
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Reporter genes are a type of protein-coding gene that are often tagged to a gene of interest. Once inside a target cell, reporter genes usually produce visually identifiable characteristics like fluorescence and luminescence when expressed along with the gene of interest. Thus, reporter genes “report” the presence or absence of genes of interest in an organism, determine the gene expression pattern, or track the physical location of a DNA segment or protein in the cell.
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Updated: Oct 19, 2025

Inherent Dynamics Visualizer, an Interactive Application for Evaluating and Visualizing Outputs from a Gene Regulatory Network Inference Pipeline
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Deepgmd: A Graph-Neural-Network-Based Method to Detect Gene Regulator Module.

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    Summary
    This summary is machine-generated.

    This study introduces a novel graph neural network approach for identifying gene regulatory modules from omics data. The method effectively detects gene subgroups, outperforming existing techniques by analyzing gene co-expression networks.

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    Area of Science:

    • Bioinformatics
    • Computational Biology
    • Systems Biology

    Background:

    • Gene regulatory module mining is crucial for understanding biological mechanisms from omics data.
    • Traditional methods transform gene expression data into co-expression networks, framing module detection as community structure identification.
    • Network representation learning, particularly graph neural networks (GNNs), offers advanced optimization for this problem.

    Purpose of the Study:

    • To introduce a novel GNN-based method for gene regulatory module detection.
    • To evaluate the impact of overlapping modules on detection accuracy.
    • To compare the proposed algorithm's performance against current mainstream methods.

    Main Methods:

    • Gene expression profile data was transformed into a gene co-expression network.
    • A graph neural network approach was employed to identify community structures (gene modules).
    • Two variants, Deepgmd_cluster (non-overlapping) and Deepgmd (overlapping), were developed and compared.

    Main Results:

    • The proposed GNN-based method demonstrated superior performance in identifying gene modules compared to existing approaches.
    • Analysis confirmed the effectiveness of the algorithm in detecting biologically relevant gene subgroups.
    • Enrichment and pathway analyses using GO and KEGG databases provided insights into the biological functions of identified modules.

    Conclusions:

    • The GNN-based approach, DeepGMD, is a powerful tool for gene regulatory module mining.
    • The study highlights the advantages of using network representation learning for analyzing complex biological networks.
    • The findings facilitate a deeper understanding of gene interactions and regulatory mechanisms.