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Related Concept Videos

Cis-regulatory Sequences02:02

Cis-regulatory Sequences

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Cis-regulatory sequences are short fragments of non-coding DNA that are present on the same chromosomes as the genes that they regulate. These fragments serve as binding sites for transcriptional regulators, proteins that are responsible for controlling gene transcription and differential gene expression across cell types in eukaryotes. Cis-regulatory sequences can be close to the gene of interest or thousands of bases away in the DNA sequence; however, those sequences that are further away are...
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Combinatorial gene control is the synergistic action of several transcriptional factors to regulate the expression of a single gene. The absence of one or more of these factors may lead to a significant difference in the level of gene expression or repression.
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Related Experiment Video

Updated: Jun 22, 2025

Inherent Dynamics Visualizer, an Interactive Application for Evaluating and Visualizing Outputs from a Gene Regulatory Network Inference Pipeline
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Improved Fuzzy Cognitive Maps for Gene Regulatory Networks Inference Based on Time Series Data.

Marzieh Emadi, Farsad Zamani Boroujeni, Jamshid Pirgazi

    IEEE/ACM Transactions on Computational Biology and Bioinformatics
    |July 4, 2024
    PubMed
    Summary
    This summary is machine-generated.

    This study introduces a novel hybrid approach combining fuzzy cognitive maps and compressed sensing to accurately identify gene interactions from complex microarray data. The method enhances robustness against noise, outperforming existing techniques in gene regulation network analysis.

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    Area of Science:

    • Bioinformatics
    • Computational Biology
    • Systems Biology

    Background:

    • Microarray data offers extensive gene expression insights but presents analytical challenges.
    • High dimensionality (many genes) and low sample numbers, coupled with data noise, complicate gene regulation network inference.
    • Existing computational methods struggle to effectively address these limitations in gene interaction analysis.

    Purpose of the Study:

    • To develop a robust hybrid computational method for identifying gene interactions.
    • To enhance the accuracy of gene regulation network inference from noisy, high-dimensional microarray data.
    • To improve upon existing methods for analyzing complex gene expression datasets.

    Main Methods:

    • A hybrid approach integrating fuzzy cognitive maps (FCM) with compressed sensing (CS) was developed.
    • Ensemble Kalman Filter (EnKF) combined with CS was employed to learn the FCM parameters.
    • This integration aimed to create a fuzzy cognitive map robust to noise in gene expression data.

    Main Results:

    • The proposed hybrid method demonstrated superior performance in identifying gene interactions.
    • Evaluated using metrics like SSmean, Data Error, and accuracy, the method outperformed established techniques (LASSOFCM, KFRegular, CMI2NI).
    • The Ensemble Kalman filtered compressed sensing approach significantly improved the robustness of the fuzzy cognitive map against data noise.

    Conclusions:

    • The hybrid FCM and CS method offers a powerful and robust solution for gene regulation network inference.
    • This approach effectively addresses the challenges of high dimensionality and noisy data in microarray analysis.
    • The findings suggest a significant advancement in computational methods for understanding gene interactions and biological pathways.