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Related Concept Videos

Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving01:29

Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving

Mechanistic models play a crucial role in algorithms for numerical problem-solving, particularly in nonlinear mixed effects modeling (NMEM). These models aim to minimize specific objective functions by evaluating various parameter estimates, leading to the development of systematic algorithms. In some cases, linearization techniques approximate the model using linear equations.
In individual population analyses, different algorithms are employed, such as Cauchy's method, which uses a...
Mechanistic Models: Compartment Models in Individual and Population Analysis01:23

Mechanistic Models: Compartment Models in Individual and Population Analysis

Mechanistic models are utilized in individual analysis using single-source data, but imperfections arise due to data collection errors, preventing perfect prediction of observed data. The mathematical equation involves known values (Xi), observed concentrations (Ci), measurement errors (εi), model parameters (ϕj), and the related function (ƒi) for i number of values. Different least-squares metrics quantify differences between predicted and observed values. The ordinary least squares (OLS)...
Quantifying and Rejecting Outliers: The Grubbs Test01:02

Quantifying and Rejecting Outliers: The Grubbs Test

Sometimes, a data set can have a recorded numerical observation that greatly  deviates from the rest of the data. Assuming that the data is normally distributed, a statistical method called the Grubbs test can be used to determine whether the observation is truly an outlier.  To perform a two-tailed Grubbs test, first, calculate the absolute difference between the outlier and the mean. Then, calculate the ratio between this difference and the standard deviation of the sample. This number is...
Detection of Gross Error: The Q Test01:00

Detection of Gross Error: The Q Test

When one or more data points appear far from the rest of the data, there is a need to determine whether they are outliers and whether they should be eliminated from the data set to ensure an accurate representation of the measured value. In many cases, outliers arise from gross errors (or human errors) and do not accurately reflect the underlying phenomenon. In some cases, however, these apparent outliers reflect true phenomenological differences. In these cases, we can use statistical methods...
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...
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Methods of Medium Optimization

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Related Experiment Videos

Better models by discarding data?

K Diederichs1, P A Karplus

  • 1Faculty of Biology, University of Konstanz, M647, 78457 Konstanz, Germany. kay.diederichs@uni-konstanz.de

Acta Crystallographica. Section D, Biological Crystallography
|June 25, 2013
PubMed
Summary
This summary is machine-generated.

The correlation coefficient CC1/2 offers superior data quality assessment in macromolecular X-ray crystallography compared to traditional R values. It accurately reflects model quality, even when data filtering improves merging R values but reduces overall model quality.

Keywords:
R valuecorrelation coefficientdata qualitymodel qualityoutlier rejection

Related Experiment Videos

Area of Science:

  • Macromolecular X-ray crystallography
  • Structural biology
  • Biophysics

Background:

  • Macromolecular X-ray crystallography datasets often have high multiplicity, allowing for data quality assessment through measurement consistency.
  • Traditional merging R values are commonly used but have limitations in accurately reflecting data and model quality.

Purpose of the Study:

  • To characterize the properties of the correlation coefficient CC1/2 for assessing data quality in X-ray crystallography.
  • To compare the performance of CC1/2 against conventional indicators like merging R values in practical scenarios.
  • To evaluate the impact of data-filtering strategies on refined model quality.

Main Methods:

  • Characterization of the correlation coefficient CC1/2 and its derived quantity CC*.
  • Comparison of CC1/2 with merging R values using experimental data sets.
  • Controlled 'paired-refinement' tests involving merging data from different crystals and selective data rejection.

Main Results:

  • CC1/2 demonstrates superior properties compared to merging R values for assessing data quality.
  • Selective data rejection can improve merging R values but leads to lower quality refined models.
  • CC1/2 accurately reflects the quality of refined models across different data-handling strategies.

Conclusions:

  • Practices aimed at improving merging R values by filtering data can be detrimental to refined model quality.
  • CC1/2 is a reliable indicator for evaluating data quality and its impact on structural model refinement in crystallography.
  • The study highlights the limitations of traditional R values and promotes CC1/2 as a more robust metric.