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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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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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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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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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The confirmation bias is the tendency to focus on information that confirms our existing beliefs and ignore information that is inconsistent with our expectations. For example, if you think that your professor is not very nice, you notice all of the instances of rude behavior exhibited by the professor while ignoring the countless pleasant interactions he is involved in on a daily basis. Have you ever fallen prey to the confirmation bias, either as the source or target of such bias?
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Inherent Dynamics Visualizer, an Interactive Application for Evaluating and Visualizing Outputs from a Gene Regulatory Network Inference Pipeline
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Gaining confidence in inferred networks.

Léo P M Diaz1, Michael P H Stumpf2

  • 1School of BioSciences and School of Mathematics and Statistics, University of Melbourne, Parkville, VIC, 3010, Australia.

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Summary
This summary is machine-generated.

Validating biological networks is difficult. This study introduces functional assortativity, a new heuristic method, to assess the reliability of inferred gene regulatory networks by analyzing interaction patterns.

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Area of Science:

  • Computational Biology
  • Systems Biology
  • Bioinformatics

Background:

  • Network inference for biological systems, such as gene regulatory networks, is challenging due to high uncertainty and potential for false interactions.
  • Current validation methods often rely on simulated data, lacking direct assessment against real biological networks.

Purpose of the Study:

  • To develop a reliable validation method for biological network inference using real-world data.
  • To introduce and evaluate 'functional assortativity' as a heuristic for assessing inferred network quality.

Main Methods:

  • Quantifying mixing patterns in biological networks using the assortativity coefficient.
  • Applying the concept of functional assortativity to biological networks, hypothesizing that interactions are more likely between functionally similar nodes.

Main Results:

  • Demonstrated that functional assortativity can be reliably quantified.
  • Showcased functional assortativity as an informative metric for comparing the performance of different network inference algorithms.

Conclusions:

  • Functional assortativity provides a robust approach for validating inferred biological networks.
  • This heuristic offers a valuable tool for increasing confidence in network inference methods when applied to real biological data.