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Related Concept Videos

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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Confidence Intervals01:21

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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.
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Interpretation of Confidence Intervals01:19

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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.
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Uncertainty: Confidence Intervals00:54

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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 Interval for Estimating Population Mean01:25

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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.
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Statistical Software for Data Analysis and Clinical Trials01:12

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Statistical software is pivotal in data analysis and clinical trials by providing tools to analyze data, draw conclusions, and make predictions. These software packages range from simple data management applications to complex analytical platforms, supporting various statistical tests, models, and simulation techniques. Their significance lies in their ability to handle vast amounts of data with precision and efficiency, enabling researchers to validate hypotheses, identify trends, and make...
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Related Experiment Video

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Use of Three-Dimensional Imaging Reconstruction Software as a Training Tool for Cranial Vena Cava Venipuncture in the Ferret
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SimPhospho: a software tool enabling confident phosphosite assignment.

Veronika Suni1,2, Tomi Suomi2, Tomoya Tsubosaka3

  • 1TUCS - Turku Centre for Computer Science, FI-20500 Turku, Finland.

Bioinformatics (Oxford, England)
|March 30, 2018
PubMed
Summary

SimPhospho software enhances phosphosite assignment accuracy using simulated phosphopeptide spectral libraries. This tool improves phosphoproteome identification and reporting by validating mass spectrometry data.

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Area of Science:

  • Proteomics
  • Mass Spectrometry
  • Bioinformatics

Background:

  • Mass spectrometry is widely used for phosphosite identification but faces challenges in unambiguous assignment.
  • Accurate validation of phosphorylation sites is critical as they act as molecular switches.

Purpose of the Study:

  • To introduce SimPhospho, a software tool for simulating phosphopeptide tandem mass spectra.
  • To improve the usability and performance of a previously developed phosphosite assignment method.

Main Methods:

  • Development of SimPhospho software for simulating phosphopeptide tandem mass spectra.
  • Integration of simulated spectral libraries for validating and supplementing database search results.

Main Results:

  • SimPhospho provides a fast and user-friendly tool for accurate phosphopeptide mass spectra simulation.
  • The software aids in reliable phosphoproteome identification and reporting.
  • SimPhospho is compatible with the Trans-Proteomic Pipeline for integrated data analysis.

Conclusions:

  • SimPhospho software enhances the accuracy and reliability of phosphosite assignment.
  • The tool promotes wider adoption of simulated spectral libraries in phosphoproteomics research.
  • SimPhospho is freely available for multiple operating systems.