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Related Concept Videos

What is Gene Expression?01:42

What is Gene Expression?

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Overview
Gene expression is the process in which DNA directs the synthesis of functional products, that is, proteins. Cells can regulate gene expression at various stages. It allows organisms to generate different cell types and enables cells to adapt to internal and external factors.
Genetic Information Flows from DNA to RNA to Protein
A gene is a stretch of DNA that serves as the blueprint for functional RNAs and proteins. Since DNA is made up of nucleotides and proteins consist of amino...
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What is Gene Expression?01:36

What is Gene Expression?

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A gene is a stretch of DNA that serves as the blueprint for functional RNAs and proteins. Since DNA is comprised  of nucleotides and proteins are comprised of amino acids, a mediator is required to convert the information encoded in DNA into proteins. This mediator is the messenger RNA (mRNA). mRNA copies the blueprint from DNA by a process called transcription. In eukaryotes, transcription occurs in the nucleus by complementary base-pairing with the DNA template. The mRNA is then...
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Chromatin Position Affects Gene Expression02:35

Chromatin Position Affects Gene Expression

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Chromatin is the massive complex of DNA and proteins packaged inside the nucleus. The complexity of chromatin folding and how it is packaged inside the nucleus greatly influences  access to genetic information. Generally, the nucleus' periphery is considered transcriptionally repressive, while the cell's interior is considered a transcriptionally active area. 
Topologically Associated Domains (TADs)
The 3-dimensional positioning of chromatin in the nucleus influences the...
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Cell Specific Gene Expression01:58

Cell Specific Gene Expression

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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...
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mRNA Stability and Gene Expression02:51

mRNA Stability and Gene Expression

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The structure and stability of mRNA molecules regulates gene expression, as mRNAs are a key step in the pathway from gene to protein. In eukaryotes, the half-life of mRNA varies from a few minutes up to several days. mRNA stability is essential in growth and development. The absence of the proteins regulating its stability, such as tristetraprolin in mice, can cause systemic issues, including bone marrow overgrowth, inflammation, and autoimmunity.
Cis-acting Elements involved in mRNA stability
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Gene Flow02:39

Gene Flow

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Gene flow is the transfer of genes among populations, resulting from either the dispersal of gametes or from the migration of individuals.
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Related Experiment Video

Updated: Feb 13, 2026

Using an Automated Cell Counter to Simplify Gene Expression Studies: siRNA Knockdown of IL-4 Dependent Gene Expression in Namalwa Cells
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Classification and Biomarker Genes Selection for Cancer Gene Expression Data Using Random Forest.

Malihe Ram1, Ali Najafi2, Mohammad Taghi Shakeri1

  • 1Dept. of Biostatistics, Public Health School, Mashhad University of Medical Sciences, Mashhad, Iran.

Iranian Journal of Pathology
|March 23, 2018
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Random Forest analysis effectively identifies key cancer biomarkers from gene expression data. This approach aids in cancer diagnosis and treatment by pinpointing crucial genes for various cancer types.

Keywords:
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Area of Science:

  • Genomics
  • Bioinformatics
  • Cancer Research

Background:

  • Gene expression data from microarray and next-generation sequencing (NGS) are crucial for identifying molecular patterns in cancer.
  • Analyzing large-scale gene expression datasets presents challenges in identifying cancer-associated biomarkers.
  • The random forest (RF) algorithm is well-suited for analyzing high-dimensional data (large-p, small-n) common in genomics.

Purpose of the Study:

  • To apply the random forest (RF) algorithm for selecting and ranking genes as potential biomarkers for cancer diagnosis and treatment.
  • To identify key genes associated with colon, leukemia, and prostate cancers using gene expression data.
  • To evaluate the predictive performance and biological relevance of the identified genes.

Main Methods:

  • Collected microarray gene expression data for colon, leukemia, and prostate cancers from public repositories.
  • Preprocessed the datasets using the limma package and applied the RF classification method in R.
  • Evaluated the selected genes against previous studies and assessed their functional roles in cancer pathways.

Main Results:

  • The RF method efficiently identified minimal gene sets with high predictive accuracy for each cancer type.
  • For colon cancer, genes like DIEXF, GUCA2A, CA7, and IGHA1 were selected (87.39% accuracy).
  • Prostate cancer yielded SNCA, USP20, and SNRPA1 (73.33% accuracy), while leukemia identified BAG4, ANKHD1-EIF4EBP3, PLXNC1, and PCDH9 (100% accuracy).

Conclusions:

  • The identified genes are largely consistent with previously reported cancer-related genes.
  • These key genes play significant roles in the cellular transformation from normal to cancerous states.
  • The RF approach demonstrates effectiveness in biomarker discovery for cancer research.