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Related Concept Videos

Cell Specific Gene Expression01:58

Cell Specific Gene Expression

Multicellular organisms contain a variety of structurally and functionally distinct cell types, but the DNA in all the cells originated from the same parent cells. The differences in the cells can be attributed to the differential gene expression. Liver cells, whose functions include detoxification of blood, production of bile to metabolize fats, and synthesis of proteins essential for metabolism, must express a specific set of genes to perform their functions. Gene expression also varies with...
Cell Specific Gene Expression01:58

Cell Specific Gene Expression

Multicellular organisms contain a variety of structurally and functionally distinct cell types, but the DNA in all the cells originated from the same parent cells. The differences in the cells can be attributed to the differential gene expression. Liver cells, whose functions include detoxification of blood, production of bile to metabolize fats, and synthesis of proteins essential for metabolism, must express a specific set of genes to perform their functions. Gene expression also varies with...
Epigenetic Regulation01:37

Epigenetic Regulation

Epigenetic changes alter the physical structure of the DNA without changing the genetic sequence and often regulate whether genes are turned on or off. This regulation ensures that each cell produces only proteins necessary for its function. For example, proteins that promote bone growth are not produced in muscle cells. Epigenetic mechanisms play an essential role in healthy development. Conversely, precisely regulated epigenetic mechanisms are disrupted in diseases like cancer.
X-chromosome...
Epigenetic Regulation01:46

Epigenetic Regulation

Epigenetic mechanisms play an essential role in healthy development. Conversely, precisely regulated epigenetic mechanisms are disrupted in diseases like cancer.
Cancer-Critical Genes I: Proto-oncogenes01:33

Cancer-Critical Genes I: Proto-oncogenes

Genes usually encode proteins necessary for the proper functioning of a healthy cell. Mutations can often cause changes to the gene expression pattern, thereby altering the phenotype.
When the function of certain critical genes, especially those involved in cell cycle regulation and cell growth signaling cascades, gets disrupted, it upsets the cell cycle progression. Such cells with unchecked cell cycles start proliferating uncontrollably and eventually develop into tumors.
Such genes that act...
Cancer-Critical Genes I: Proto-oncogenes01:33

Cancer-Critical Genes I: Proto-oncogenes

Genes usually encode proteins necessary for the proper functioning of a healthy cell. Mutations can often cause changes to the gene expression pattern, thereby altering the phenotype.
When the function of certain critical genes, especially those involved in cell cycle regulation and cell growth signaling cascades, gets disrupted, it upsets the cell cycle progression. Such cells with unchecked cell cycles start proliferating uncontrollably and eventually develop into tumors.
Such genes that act...

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Related Experiment Video

Updated: Jun 25, 2026

Analyzing Tumor Gene Expression Factors with the CorExplorer Web Portal
08:00

Analyzing Tumor Gene Expression Factors with the CorExplorer Web Portal

Published on: October 11, 2019

Context-specific gene regulations in cancer gene expression data.

Ina Sen1, Michael P Verdicchio, Sungwon Jung

  • 1School of Computing and Informatics, Arizona State University, 699 South Mill Avenue, Suite 553, Tempe, AZ 85281, USA.

Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing
|February 13, 2009
PubMed
Summary

Cellular Context Mining infers gene regulatory networks (GRNs) specific to cellular states from expression data. This method reveals context-specific genomic information, offering advantages over traditional techniques for cancer patient data analysis.

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Area of Science:

  • Genomics
  • Systems Biology
  • Bioinformatics

Background:

  • Understanding cellular states requires analyzing genomic regulation.
  • Standard network-learning methods often overlook cellular system adaptability.
  • Cancer subtypes exhibit distinct genomic regulatory patterns.

Purpose of the Study:

  • To develop a method for inferring context-specific gene regulatory networks (GRNs).
  • To demonstrate the advantages of Cellular Context Mining over existing techniques.
  • To analyze gene expression data from cancer patients.

Main Methods:

  • Utilized Cellular Context Mining, a mathematical model for contextual genomic regulation.
  • Applied the method to steady-state gene expression microarray data.
  • Compared results with clustering and Bayesian network approaches.

Main Results:

  • Generated GRNs that are specific to varying cellular contexts within the data.
  • Successfully annotated inferred GRNs with context-specific genomic information.
  • Demonstrated superior performance compared to traditional methods for cancer data.

Conclusions:

  • Cellular Context Mining effectively models context-specific genomic regulation.
  • Context-specific GRNs provide deeper biological insights than non-contextual methods.
  • This approach enhances the analysis of complex biological systems like cancer.