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Related Concept Videos

Combinatorial Gene Control02:33

Combinatorial Gene Control

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.
The expression of more than 30,000 genes is controlled by approximately 2000-3000 transcription factors. This is possible because a single transcription factor can recognize more than one regulatory sequence. The specificity in gene...
Cooperative Binding of Transcription Regulators02:13

Cooperative Binding of Transcription Regulators

Transcriptional regulators bind to specific cis-regulatory sequences in the DNA to regulate gene transcription. These cis-regulatory sequences are very short, usually less than ten nucleotide pairs in length. The short length means that there is a high probability of the exact same sequence randomly occurring throughout the genome.  Since regulators can also bind to groups of similar sequences, this further increases the chances of random binding. Transcriptional regulators form dimers that...
Cooperative Binding of Transcription Regulators02:13

Cooperative Binding of Transcription Regulators

Transcriptional regulators bind to specific cis-regulatory sequences in the DNA to regulate gene transcription. These cis-regulatory sequences are very short, usually less than ten nucleotide pairs in length. The short length means that there is a high probability of the exact same sequence randomly occurring throughout the genome.  Since regulators can also bind to groups of similar sequences, this further increases the chances of random binding. Transcriptional regulators form dimers that...
Cis-regulatory Sequences02:02

Cis-regulatory Sequences

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...
Cis-regulatory Sequences02:02

Cis-regulatory Sequences

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...
Regulation of Expression at Multiple Steps01:23

Regulation of Expression at Multiple Steps

The gene expression in cells is regulated at different stages: (i) transcription, (ii) RNA processing, (iii) RNA localization, and (iv) translation. Transcriptional regulation is mediated by regulatory proteins such as transcription factors, activators, or repressors—these control gene expression by initiating or inhibiting the transcription of genes. Once a precursor or pre-mRNA is produced, it undergoes post-transcriptional modification, including 5' capping, splicing, and the addition of a...

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Related Experiment Video

Updated: Jun 25, 2026

Inherent Dynamics Visualizer, an Interactive Application for Evaluating and Visualizing Outputs from a Gene Regulatory Network Inference Pipeline
10:44

Inherent Dynamics Visualizer, an Interactive Application for Evaluating and Visualizing Outputs from a Gene Regulatory Network Inference Pipeline

Published on: December 7, 2021

A Novel Framework for Gene Regulatory Network Inference Integrating Bidirectional Mamba and Dual Contrastive

Kan Zhang1, Mugang Lin1,2,3, Lingzhi Zhu4

  • 1College of Computer Science and Technology, Hengyang Normal University, Hengyang 421002, China.

ACS Synthetic Biology
|June 23, 2026
PubMed
Summary
This summary is machine-generated.

BMGRN reconstructs gene regulatory networks (GRNs) with directionality and regulation types by integrating bidirectional state space modeling and contrastive learning. This framework enhances GRN inference accuracy, especially for complex, large-scale networks.

Keywords:
Kolmogorov-Arnold networksdirected graph embeddinggene regulatory networkgraph neural networkregulatory typestate space model

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Generating the Transcriptional Regulation View of Transcriptomic Features for Prediction Task and Dark Biomarker Detection on Small Datasets
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Generating the Transcriptional Regulation View of Transcriptomic Features for Prediction Task and Dark Biomarker Detection on Small Datasets

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A Novel Bayesian Change-point Algorithm for Genome-wide Analysis of Diverse ChIPseq Data Types
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A Novel Bayesian Change-point Algorithm for Genome-wide Analysis of Diverse ChIPseq Data Types

Published on: December 10, 2012

Related Experiment Videos

Last Updated: Jun 25, 2026

Inherent Dynamics Visualizer, an Interactive Application for Evaluating and Visualizing Outputs from a Gene Regulatory Network Inference Pipeline
10:44

Inherent Dynamics Visualizer, an Interactive Application for Evaluating and Visualizing Outputs from a Gene Regulatory Network Inference Pipeline

Published on: December 7, 2021

Generating the Transcriptional Regulation View of Transcriptomic Features for Prediction Task and Dark Biomarker Detection on Small Datasets
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Generating the Transcriptional Regulation View of Transcriptomic Features for Prediction Task and Dark Biomarker Detection on Small Datasets

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A Novel Bayesian Change-point Algorithm for Genome-wide Analysis of Diverse ChIPseq Data Types
12:39

A Novel Bayesian Change-point Algorithm for Genome-wide Analysis of Diverse ChIPseq Data Types

Published on: December 10, 2012

Area of Science:

  • Computational Biology
  • Systems Biology
  • Bioinformatics

Background:

  • Reconstructing gene regulatory networks (GRNs) with directionality and regulatory types is a key challenge.
  • Existing methods struggle with complex topological structures and representation dimensionality collapse in skewed GRNs.

Purpose of the Study:

  • To propose BMGRN, a unified framework for reconstructing directional GRNs with regulation types.
  • To address limitations in capturing global/local information and representation dimensionality in GRN inference.

Main Methods:

  • Utilizing an enhanced bidirectional Mamba2 architecture for long-range dependency and asymmetric interaction capture.
  • Integrating a dual contrastive learning mechanism to prevent oversmoothing and dimensional collapse.
  • Employing a KAN-based convolutional predictor for adaptive nonlinear dependency learning.

Main Results:

  • BMGRN demonstrates superior performance on multiple benchmark datasets for GRN inference.
  • The framework effectively captures complex topological structures and regulatory modes.
  • Achieves high accuracy in large-scale GRN reconstruction.

Conclusions:

  • BMGRN offers a powerful and unified approach for directional GRN reconstruction with regulation types.
  • The method shows significant potential for advancing large-scale GRN inference.
  • The proposed framework effectively integrates state space modeling and contrastive learning for improved biological network analysis.