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Related Concept Videos

Correlation of Experimental Data01:23

Correlation of Experimental Data

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Dimensional analysis simplifies complex physical problems and guides experimental investigations, but it does not provide complete solutions. It identifies the dimensionless groups that influence a phenomenon, but experimental data is needed to establish the specific relationships and validate theoretical predictions.
For example, a spherical particle moving through a viscous fluid experiences drag. Dimensional analysis shows that the drag force depends on the particle's diameter, velocity,...
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Coefficient of Correlation01:12

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The correlation coefficient, r, developed by Karl Pearson in the early 1900s, is numerical and provides a measure of strength and direction of the linear association between the independent variable x and the dependent variable y.
If you suspect a linear relationship between x and y, then r can measure how strong the linear relationship is.
What the VALUE of r tells us:
The value of r is always between –1 and +1: –1 ≤ r ≤ 1.
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Correlation means that there is a relationship between two or more variables (such as ice cream consumption and crime), but this relationship does not necessarily imply cause and effect. When two variables are correlated, it simply means that as one variable changes, so does the other. We can measure correlation by calculating a statistic known as a correlation coefficient. A correlation coefficient is a number from -1 to +1 that indicates the strength and direction of the relationship between...
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In statistics, correlation describes the degree of association between two variables. In the subfield of linear regression, correlation is mathematically expressed by the correlation coefficient, which describes the strength and direction of the relationship between two variables. The coefficient is symbolically represented by 'r' and ranges from -1 to +1. A positive value indicates a positive correlation where the two variables move in the same direction. A negative value suggests a...
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The correlation coefficient, r, developed by Karl Pearson in the early 1900s, is numerical and provides a measure of strength and direction of the linear association between the independent variable, x, and the dependent variable, y. Hence, it is also known as the Pearson product-moment correlation coefficient. It can be calculated using the following equation:
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In statistics, two variables are said to be correlated if the values of one variable are associated with the other variable. Depending on the relationship between two variables, correlation can be of three types– positive correlation, negative correlation, and zero correlation.
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Updated: Nov 8, 2025

Analyzing Tumor Gene Expression Factors with the CorExplorer Web Portal
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Correlation AnalyzeR: functional predictions from gene co-expression correlations.

Henry E Miller1,2, Alexander J R Bishop3,4,5

  • 1Greehey Children's Cancer Research Institute, University of Texas Health At San Antonio, San Antonio, TX, 78229, USA. millerh1@livemail.uthscsa.edu.

BMC Bioinformatics
|April 21, 2021
PubMed
Summary
This summary is machine-generated.

Correlation AnalyzeR offers a user-friendly web tool to explore gene co-expression patterns in specific tissues and diseases. This helps predict gene functions and relationships, aiding biological discovery.

Keywords:
Co-expressionData miningGene correlationGene functionR shinyRNA-SeqSystems biologyWeb application

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Area of Science:

  • Bioinformatics
  • Systems Biology
  • Genomics

Background:

  • Gene co-expression analysis is crucial for predicting gene function in biological contexts.
  • Existing databases lack context-specificity and sophisticated analysis tools.
  • Limited visualization hinders adoption by biologists without computational expertise.

Purpose of the Study:

  • To develop a user-friendly web interface for exploring gene co-expression correlations.
  • To predict gene functions, gene-gene relationships, and gene set topology.
  • To provide context-specific (tissue and disease) genome-wide co-expression data.

Main Methods:

  • Developed Correlation AnalyzeR, a web application and R package.
  • Integrated a database of tissue and disease-specific co-expression correlations.
  • Implemented sophisticated computational tools with user-friendly visualizations.

Main Results:

  • Correlation AnalyzeR provides flexible access to context-specific co-expression data.
  • The tool enables functional predictions and visualization of gene relationships.
  • Demonstrated utility in exploring BRCA1-NRF2 interplay in bone cancer, generating novel hypotheses.

Conclusions:

  • Correlation AnalyzeR facilitates the exploration of understudied genes and relationships.
  • Enables the discovery of novel biological insights.
  • Accessible as a web application and standalone R package.