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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. 
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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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Cell Specific Gene Expression01:58

Cell Specific Gene Expression

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No description available
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What Are Outliers?01:12

What Are Outliers?

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Outliers are observed data points that are far from the least squares line. They have unusual values and need to be examined carefully. Though an outlier may result from erroneous data, at other times, it may hold valuable information about the population under study and should be included in the data. Hence, it is crucial to examine what causes a data point to be an outlier.
The z score is used to find outliers or unusual values. It should be noted that any values beyond -2 and +2 are...
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Using Human Differentially Expressed Gene Lists to Perform Downstream Pathway Enrichment Analysis and Target Prioritization
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Cancer outlier differential gene expression detection.

Baolin Wu1

  • 1Division of Biostatistics, School of Public Health, University of Minnesota, A460 Mayo Building, MMC 303, Minneapolis, MN 55455, USA. baolin@biostat.umn.edu

Biostatistics (Oxford, England)
|October 6, 2006
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Summary

We developed a new statistical method, the outlier robust t-statistic (ORT), to find cancer genes with unusual expression levels. This method improves detection power and reduces false positives in cancer gene discovery.

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Area of Science:

  • Biostatistics
  • Computational Biology
  • Genomics

Background:

  • Detecting differential gene expression is crucial in cancer research.
  • Identifying genes with outlier expression in subsets of samples is key for discovering oncogenes.
  • Existing methods like outlier profile analysis and outlier sum statistic have limitations.

Purpose of the Study:

  • To propose a novel statistical method, the outlier robust t-statistic (ORT), for detecting cancer genes with outlier expression.
  • To compare the performance of ORT against existing methods using real and simulated data.

Main Methods:

  • Development of the outlier robust t-statistic (ORT).
  • Comparative analysis using real-world cancer datasets and simulation studies.
  • Evaluation metrics included detection power and false discovery rates.

Main Results:

  • The outlier robust t-statistic (ORT) demonstrated superior performance compared to existing methods.
  • ORT often achieved higher detection power for cancer-related genes.
  • ORT resulted in lower false discovery rates, improving the accuracy of gene identification.

Conclusions:

  • The outlier robust t-statistic (ORT) is an effective and powerful method for identifying cancer genes with outlier expression.
  • ORT offers an improved approach for cancer gene discovery, particularly for oncogenes activated in subsets of patients.