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Related Concept Videos

Prediction Intervals01:03

Prediction Intervals

2.5K
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. 
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Improving Translational Accuracy02:07

Improving Translational Accuracy

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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...
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Improving Translational Accuracy02:07

Improving Translational Accuracy

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Confidence Coefficient01:24

Confidence Coefficient

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The confidence coefficient is also known as the confidence level or degree of confidence. It is the percent expression for the probability, 1-α, that the confidence interval contains the true population parameter assuming that the confidence interval is obtained after sufficient unbiased sampling; for example, if the CL = 90%, then in 90 out of 100 samples the interval estimate will enclose the true population parameter. Here α is the area under the curve, distributed equally under...
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Uncertainty: Confidence Intervals00:54

Uncertainty: Confidence Intervals

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The confidence interval is the range of values around the mean that contains the true mean. It is expressed as a probability percentage. The interpretation of a 95% confidence interval, for instance, is that the statistician is 95% confident that the true mean falls within the interval. The upper and lower limits of this range are known as confidence limits. The confidence limits for the true mean are estimated from the sample's mean, the standard deviation, and the statistical factor...
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Confidence Intervals01:21

Confidence Intervals

9.3K
An unbiased point estimate is often insufficient to predict a population estimate, such as population mean or population proportion. In this scenario, a confidence interval is used. A confidence interval is an estimate similar to a sample proportion. However, unlike the point estimate which is a single value, the confidence interval contains a range of values. These values have lower and upper limits, known as confidence limits, and can be designated as L1 and L2, respectively.
A confidence...
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Related Experiment Videos

Developing scientific confidence in HTS-derived prediction models: lessons learned from an endocrine case study.

Louis Anthony Cox1, Douglas Popken1, M Sue Marty2

  • 1Cox Associates, 503 Franklin St, Denver, CO 80218, USA.

Regulatory Toxicology and Pharmacology : RTP
|May 22, 2014
PubMed
Summary

High throughput screening (HTS) shows promise for endocrine disruption assessment, accurately predicting androgen and estrogen effects. However, thyroid and steroidogenesis predictions require further research for regulatory use.

Keywords:
Adverse outcome pathwaysEndocrineHigh throughput/high content assaysPrediction modelsValidation framework

Related Experiment Videos

Area of Science:

  • Toxicology
  • Endocrinology
  • Computational Biology

Background:

  • High throughput screening (HTS) and high content screening (HCS) methods offer potential for hazard and risk assessments.
  • Establishing scientific confidence in HTS/HCS methods and predictive models is crucial for regulatory acceptance.

Purpose of the Study:

  • To evaluate the predictive accuracy of HTS-derived models for in vivo endocrine endpoints (androgen, estrogen, thyroid, steroidogenesis).
  • To compare classification (fitting) versus cross-validation (prediction) models for endocrine screening assays.
  • To propose a framework for documenting scientific confidence in HTS assays and predictive models.

Main Methods:

  • Utilized a case study focusing on HTS assays and guideline in vivo endocrine screening studies.
  • Compared classification models against more robust cross-validation models.
  • Analyzed prediction accuracies for androgen (A), estrogen (E), thyroid (T), and steroidogenesis (S) endpoints.

Main Results:

  • Cross-validation models achieved balanced accuracies of 79-85% for A and E endpoints.
  • Prediction accuracies for T and S endpoints ranged from 23% to 50%.
  • HTS results show potential for prioritizing endocrine screening for A and E endpoints.

Conclusions:

  • HTS methods show promise for initial endocrine screening priorities for androgen and estrogen.
  • Further research is necessary to enhance HTS/HCS predictive models and expand their applicability for regulatory use.
  • A proposed Scientific Confidence Framework can aid in evaluating HTS assay and model reliability for regulatory decisions.