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Related Concept Videos

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...
Confidence Intervals01:21

Confidence Intervals

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...
Uncertainty: Confidence Intervals00:54

Uncertainty: Confidence Intervals

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 't,' or...
Confidence Coefficient01:24

Confidence Coefficient

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 both the...
Interpretation of Confidence Intervals01:19

Interpretation of Confidence Intervals

A confidence interval is a better estimate of the population than a point estimate, as it uses a range of values from a sample instead of a single value.
Confidence intervals have confidence coefficients that are crucial for their interpretation. The most common confidence coefficients are 0.90, 0.95, and 0.99, which can be written as percentages–90%, 95%, and 99%, respectively.
Suppose a person calculates a confidence interval with a confidence coefficient of 0.95. In that case, they can...
Confidence Interval for Estimating Population Mean01:25

Confidence Interval for Estimating Population Mean

A point estimate of the population mean is obtained from a single sample. Such a point estimate does not represent a population well because it needs to account for variability in the population. Single point estimate can also be biased despite the sample being selected randomly. Thus, a point estimate is often unreliable. A confidence interval is needed to reduce this unreliability.
A confidence interval for the mean is a range of values that provides an estimate of the population mean. As the...

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Related Experiment Video

Updated: Jun 6, 2026

Superior Auto-Identification of Trypanosome Parasites by Using a Hybrid Deep-Learning Model
08:20

Superior Auto-Identification of Trypanosome Parasites by Using a Hybrid Deep-Learning Model

Published on: October 27, 2023

Nearest template prediction: a single-sample-based flexible class prediction with confidence assessment.

Yujin Hoshida1

  • 1Cancer Program, Broad Institute of Massachusetts Institute of Technology and Harvard University, Cambridge, Massachusetts, USA. hoshida@broadinstitute.org

Plos One
|December 3, 2010
PubMed
Summary

Gene-expression signatures can predict disease and outcomes, but clinical use is limited. The nearest template prediction (NTP) method offers a flexible solution for reliable, single-patient predictions across diverse datasets.

Related Experiment Videos

Last Updated: Jun 6, 2026

Superior Auto-Identification of Trypanosome Parasites by Using a Hybrid Deep-Learning Model
08:20

Superior Auto-Identification of Trypanosome Parasites by Using a Hybrid Deep-Learning Model

Published on: October 27, 2023

Area of Science:

  • Bioinformatics
  • Computational Biology
  • Genomics

Background:

  • Gene-expression signatures are crucial for disease classification and outcome prediction.
  • Clinical adoption is hindered by validation challenges, assay variability, and platform inconsistencies.
  • Reliable, single-patient predictions with confidence measures are essential for clinical decision support.

Purpose of the Study:

  • To introduce a flexible prediction method addressing limitations in gene-expression-based clinical applications.
  • To enable reliable, single-patient disease classification and outcome prediction with confidence assessment.
  • To overcome challenges related to experimental variability and analytical inconsistencies.

Main Methods:

  • The nearest template prediction (NTP) method was employed for class prediction.
  • NTP utilizes a list of signature genes and a test dataset for analysis.
  • Prediction confidence is computed for each individual patient's gene-expression data.

Main Results:

  • NTP provides a convenient approach for class prediction with confidence assessment.
  • The method is robust across different platforms, species, and for multiclass predictions.
  • No optimization of analysis parameters was required for NTP application.

Conclusions:

  • NTP offers a flexible, reliable, and confidence-aware method for gene-expression-based predictions.
  • This approach facilitates the clinical deployment of gene-expression signatures for disease classification and outcome prediction.
  • NTP overcomes key obstacles hindering the translation of gene-expression signatures into clinical practice.