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Related Concept Videos

Reporter Genes02:11

Reporter Genes

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.
Commonly used reporter...
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...
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: Jul 13, 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

An effective data mining technique for reconstructing gene regulatory networks from time series expression data.

Patrick C H Ma1, Keith C C Chan

  • 1Department of Computing, The Hong Kong Polytechnic University, Hung Hom, Kowloon, Hong Kong, China. cschma@comp.polyu.edu.hk

Journal of Bioinformatics and Computational Biology
|August 11, 2007
PubMed
Summary

This study introduces a novel data mining technique for reconstructing gene regulatory networks (GRNs). The method accurately predicts gene interactions and regulatory effects, even in unseen data, advancing GRN inference.

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Area of Science:

  • Systems Biology
  • Bioinformatics
  • Computational Biology

Background:

  • DNA microarray technologies enable gene regulatory network (GRN) reconstruction.
  • Existing GRN methods often generate hypotheses for lab verification and struggle with statistical reliability and directionality.

Purpose of the Study:

  • To develop a data mining technique for inferring gene regulatory relationships from high-dimensional time series expression data.
  • To address limitations of existing methods regarding prediction in unseen samples and establishing regulatory directionality.

Main Methods:

  • A probabilistic inference approach is employed to analyze noisy, high-dimensional time series gene expression data.
  • The technique uncovers dependency relationships, identifying activation or inhibition between genes.

Main Results:

  • The proposed method effectively determines gene dependencies and regulatory effects (activation/inhibition).
  • It demonstrates predictive capability for gene behavior in unseen samples.
  • Experimental validation using real expression data confirms its effectiveness.

Conclusions:

  • The developed data mining technique offers a robust approach for GRN reconstruction.
  • It enhances the understanding of gene regulatory relationships and network structures.
  • The method provides statistically verifiable and directional insights into gene interactions.