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Related Concept Videos

DNA Microarrays02:34

DNA Microarrays

Microarrays are high-throughput and relatively inexpensive assays that can be automated to analyze large quantities of data at a time. They are used in genome-wide studies to compare gene or protein expression under two varied conditions, such as healthy and diseased states. Microarrays consist of glass or silica slides on which probe molecules are covalently attached through surface functionalization. Most commonly, the slides are prepared through the chemisorption of silanes to silica...
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Calibration Curves: Correlation Coefficient

In a linear calibration curve, there is a value called the calibration coefficient, denoted by 'r,' which measures the strength and the direction of association between two variables. The correlation coefficient value ranges from −1 to +1. A value of +1 indicates a perfect positive linear correlation, −1 denotes a perfect negative correlation, and 0 implies no correlation between the two variables. A positive correlation value establishes that as one variable increases, the other increases, and...
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Calibration Curves: Linear Least Squares

A calibration curve is a plot of the instrument's response against a series of known concentrations of a substance. This curve is used to set the instrument response levels, using the substance and its concentrations as standards. Alternatively, or additionally, an equation is fitted to the calibration curve plot and subsequently used to calculate the unknown concentrations of other samples reliably.
For data that follow a straight line, the standard method for fitting is the linear...
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Base complementarity between the three base pairs of mRNA codon and the tRNA anticodon is not a failsafe mechanism. Inaccuracies can range from a single mismatch to no correct base pairing at all. The free energy difference between the correct and nearly correct base pairs can be as small as 3 kcal/ mol. With complementarity being the only proofreading step, the estimated error frequency would be one wrong amino acid in every 100 amino acids incorporated. However, error frequencies observed in...
Improving Translational Accuracy02:07

Improving Translational Accuracy

Base complementarity between the three base pairs of mRNA codon and the tRNA anticodon is not a failsafe mechanism. Inaccuracies can range from a single mismatch to no correct base pairing at all. The free energy difference between the correct and nearly correct base pairs can be as small as 3 kcal/ mol. With complementarity being the only proofreading step, the estimated error frequency would be one wrong amino acid in every 100 amino acids incorporated. However, error frequencies observed in...

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A Rapid High-throughput Method for Mapping Ribonucleoproteins (RNPs) on Human pre-mRNA
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Ratio adjustment and calibration scheme for gene-wise normalization to enhance microarray inter-study prediction.

Chunrong Cheng1, Kui Shen, Chi Song

  • 1Department of Biostatistics, University of Pittsburgh, Pittsburgh, PA, USA.

Bioinformatics (Oxford, England)
|May 6, 2009
PubMed
Summary

Gene-wise normalization improves microarray inter-study prediction for disease diagnosis and prognosis. An analytical method adjusts for sample size imbalances, enhancing clinical utility and translational research applications.

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Area of Science:

  • Bioinformatics
  • Genomics
  • Translational Research

Background:

  • Microarray reproducibility analyses often focus on biomarker overlap or differential expression correlation.
  • Direct inter-study prediction for clinical diagnosis and prognosis is crucial but underexplored.
  • Sample-wise normalization is standard, but gene-wise normalization's role in inter-study prediction remains unclear.

Purpose of the Study:

  • To investigate the effect and necessity of gene-wise normalization in microarray inter-study prediction.
  • To develop a method for adjusting gene-wise normalization for imbalanced sample sizes.
  • To establish a calibration scheme for applying adjusted gene-wise normalization in clinical trials.

Main Methods:

  • Investigated gene-specific intensity discrepancies across studies post-sample-wise normalization.
  • Developed an analytical method to adjust gene-wise normalization for imbalanced normal vs. diseased sample sizes.
  • Validated the method using simulations and applied it to lung and prostate cancer datasets for classification and survival prediction.

Main Results:

  • Gene-wise normalization significantly improves inter-study prediction accuracy.
  • The developed adjustment method robustly enhances prediction performance, especially with imbalanced sample sizes.
  • A calibration scheme was proposed for prospective clinical trials, estimating necessary calibration samples.

Conclusions:

  • Gene-wise normalization is essential for robust inter-study microarray prediction.
  • The ratio-adjusted gene-wise normalization method offers significant improvements for clinical applications.
  • This approach enhances microarray's utility as a practical tool for disease diagnosis and prognosis.