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Root Mean Square00:57

Root Mean Square

3.8K
If in an experiment, data values have a probability of being both positive and negative, neither the arithmetic mean, the geometric mean, nor the harmonic mean can be used to calculate the central tendency of the data set. In particular, if the positive and negative values are equally likely, the arithmetic mean is close to zero.
For example, consider the velocity of gas molecules in a container. The gas molecules are moving in different directions, which might impart positive and negative...
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Punnett Squares01:00

Punnett Squares

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Overview
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Kinetic Molecular Theory: Molecular Velocities, Temperature, and Kinetic Energy03:07

Kinetic Molecular Theory: Molecular Velocities, Temperature, and Kinetic Energy

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The kinetic molecular theory qualitatively explains the behaviors described by the various gas laws. The postulates of this theory may be applied in a more quantitative fashion to derive these individual laws.
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Variation: Normal Distribution, Range, and Standard Deviation02:32

Variation: Normal Distribution, Range, and Standard Deviation

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In the field of psychology, there are several ways to organize measurements of a trait, feature, or characteristic (i.e., variables). Qualitative data, such as ethnicity, can be tabulated into a frequency count to provide information about the proportion, as well as the variety of groups in a sample or population. On the other hand, researchers can perform a wider set of calculations on quantitative data. The mean, mode, and median, for instance, are central tendency measures to identify a...
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Standard Deviation01:10

Standard Deviation

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The most commonly used measure of variation is the standard deviation. It is a numerical value measuring how far data values are from their mean. The standard deviation value is small when the data are concentrated close to the mean, exhibiting slight variation or spread. The standard deviation value is never negative, it is either positive or zero. The standard deviation is larger when the data values are more spread out from the mean, which means the data values are exhibiting more variation.
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Mean Absolute Deviation01:13

Mean Absolute Deviation

3.3K
The mean absolute deviation is also a measure of the variability of data in a sample. It is the absolute value of the average difference between the data values and the mean.
Let us consider a dataset containing the number of unsold cupcakes in five shops: 10, 15, 8, 7, and 10. Initially, calculate the sample mean. Then calculate the deviation, or the difference, between each data value and the mean. Next, the absolute values of these deviations are added and divided by the sample size to...
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Related Experiment Video

Updated: Jan 30, 2026

Multipronged Phenotyping Approaches to Characterize Sugarcane Root Systems
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Characterizing molecular flexibility by combining least root mean square deviation measures.

Frédéric Cazals1, Romain Tetley1

  • 1Inria (Algorithms-Biology-Structure), Université Côte d'Azur, Sophia Antipolis, France.

Proteins
|January 22, 2019
PubMed
Summary
This summary is machine-generated.

Combined RMSD offers improved structural comparisons in bioinformatics by analyzing local motifs, outperforming traditional global methods like RMSD. This new approach enhances the analysis of protein structures and conformational changes.

Keywords:
least root mean square deviationrigid motionstructural comparisonstructural motif

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Area of Science:

  • Structural Bioinformatics
  • Computational Biology
  • Biophysics

Background:

  • Root mean square deviation (RMSD) and least RMSD are standard for structural comparisons but can miss local conserved motifs.
  • Global comparison methods may obscure important local structural features in proteins.

Purpose of the Study:

  • Introduce combined RMSD, a novel similarity measure addressing limitations of global RMSD.
  • Enhance structural comparisons by integrating local structural information.

Main Methods:

  • Developed combined RMSD by merging independent least RMSD (lRMSD) measures, each using its own rigid motion.
  • Applied combined RMSD to compare quaternary structures using sequence-derived motifs (domains, SSEs).
  • Utilized combined RMSD for comparing structures based on locally aligned structural motifs.

Main Results:

  • Demonstrated combined RMSD's superiority over standard RMSD in three key applications.
  • Successfully assigned quaternary structures for hemoglobin.
  • Aided in calculating structural phylogenies for class II fusion proteins.
  • Facilitated analysis of conformational changes using rigid structural motifs.

Conclusions:

  • Combined RMSD is a valuable tool for discriminating degrees of freedom in protein structures.
  • Applicable to designing move sets and collective coordinates in structural bioinformatics.
  • Provides a more nuanced approach to structural similarity assessment.