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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...
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...

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Related Experiment Video

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Discovery of Driver Genes in Colorectal HT29-derived Cancer Stem-Like Tumorspheres
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Discovery of Driver Genes in Colorectal HT29-derived Cancer Stem-Like Tumorspheres

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Clinically driven semi-supervised class discovery in gene expression data.

Israel Steinfeld1, Roy Navon, Diego Ardigò

  • 1Agilent Laboratories, Tel Aviv, Israel.

Bioinformatics (Oxford, England)
|August 12, 2008
PubMed
Summary
This summary is machine-generated.

This study introduces semi-supervised class discovery for gene expression data, integrating biological knowledge to find relevant gene expression patterns and identify cardiovascular disease risk factors.

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Published on: July 29, 2022

Area of Science:

  • Bioinformatics
  • Computational Biology
  • Genomics

Background:

  • Unsupervised class discovery in gene expression data relies solely on statistical signals.
  • Prior biological knowledge is often desired to constrain the search space for more relevant results.

Purpose of the Study:

  • To develop a semi-supervised approach for class discovery in gene expression data.
  • To integrate clinical sample information and gene annotation to guide the discovery process.
  • To identify biologically relevant partitions and elucidate cardiovascular disease (CVD) risk factors.

Main Methods:

  • Developed a semi-supervised class discovery approach.
  • Incorporated clinical sample information to constrain the search space.
  • Utilized known gene annotations to drive the search for differential gene expression.
  • Implemented efficient algorithmics for these tasks.

Main Results:

  • The method successfully detects known clinical parameters.
  • The approach yields biologically relevant partitions.
  • Identified putative risk factors for cardiovascular disease (CVD).

Conclusions:

  • Semi-supervised class discovery enhances the biological relevance of findings in gene expression data.
  • The developed method is robust and applicable to identifying clinical parameters and disease risk factors.