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Related Concept Videos

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...
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...
Prediction Intervals01:03

Prediction Intervals

The interval estimate of any variable is known as the prediction interval. It helps decide if a point estimate is dependable.
However, the point estimate is most likely not the exact value of the population parameter, but close to it. After calculating point estimates, we construct interval estimates, called confidence intervals or prediction intervals. This prediction interval comprises a range of values unlike the point estimate and is a better predictor of the observed sample value, y. 
The...
Regression Toward the Mean01:52

Regression Toward the Mean

Regression toward the mean (“RTM”) is a phenomenon in which extremely high or low values—for example, and individual’s blood pressure at a particular moment—appear closer to a group’s average upon remeasuring. Although this statistical peculiarity is the result of random error and chance, it has been problematic across various medical, scientific, financial and psychological applications. In particular, RTM, if not taken into account, can interfere when researchers try to extrapolate results...
Predicting Products: Substitution vs. Elimination02:52

Predicting Products: Substitution vs. Elimination

When a nucleophile and an alkyl halide react, nucleophilic substitution and β-elimination reactions compete to generate products.
The following factors can influence the mechanisms competing against each other:
End Point Prediction: Gran Plot01:07

End Point Prediction: Gran Plot

A Gran plot is used to predict the equivalence volume or endpoint of a potentiometric or acid-base titration without reaching the endpoint. Typically, titration data is collected as a function of the titrant's volume up to a point less than the equivalence volume and then transformed into a linear format. The straight line is extended to the x-axis, indicating the necessary titrant volume to achieve the equivalence point.
For potentiometric titration, the Gran plot is created by plotting the...

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Related Experiment Videos

Embracing noise to improve cross-batch prediction accuracy.

Chuan Hock Koh1, Limsoon Wong

  • 1NUS Graduate School for Integrative Sciences and Engineering, Singapore. kohchuanhock@nus.edu.sg

BMC Systems Biology
|January 4, 2013
PubMed
Summary

This study introduces a novel method for microarray analysis in clinical settings. Our approach effectively removes batch effects, improving prediction accuracy and enabling smaller sample sizes for diagnosis and prognosis models.

Related Experiment Videos

Area of Science:

  • Bioinformatics
  • Computational Biology
  • Genomics

Background:

  • Microarray analysis is crucial for clinical diagnosis and prognosis models.
  • Batch effects present a significant challenge, often hindering prediction accuracy.
  • Existing batch effect removal techniques show limited success and require large sample sizes.

Purpose of the Study:

  • To develop a robust method for batch effect removal in microarray data.
  • To improve the performance of diagnosis and prognosis models in clinical settings.
  • To overcome the limitations of conventional batch effect removal techniques.

Main Methods:

  • Utilizing ranking values of microarray data.
  • Employing a bagging ensemble classifier.
  • Implementing sequential hypothesis testing to determine ensemble size dynamically.

Main Results:

  • Performance improvement observed in over 60% of cases (>0.05 AUC).
  • Performance reduction occurred in less than 2% of cases (< -0.05 AUC).
  • The method is effective with smaller training datasets and independent of test data size.

Conclusions:

  • The proposed approach offers a more reliable and feasible solution for batch effect correction in clinical microarray studies.
  • This method enhances the accuracy of diagnostic and prognostic models.
  • It addresses the limitations of existing techniques, making it suitable for real-world clinical applications.