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相关概念视频

Calculating Standard Deviation01:08

Calculating Standard Deviation

7.9K
The standard deviation is the most common measure of variation. It is a value that tells us how far a data value is from the mean value in a dataset. Further, the standard deviation is always a positive value or zero.
The standard deviation value is small when all the data is concentrated close to the mean. Here the data exhibits low variation. The standard deviation value is larger when the data values are more spread out from the mean. Here, the data displays high...
7.9K
Standard Deviation of Calculated Results01:14

Standard Deviation of Calculated Results

6.8K
Standard deviation measures the spread of data around the mean value. Many large data sets follow a Gaussian distribution, also known as a normal distribution. This distribution is bell-shaped curved, with the most frequently observed value (mean or central value) in the middle. The farther away from the central value, the greater the deviation from the central value, and the lower the frequency.
A broad Gaussian distribution curve has a wider standard deviation, representing a data set with...
6.8K
Chebyshev's Theorem to Interpret Standard Deviation01:15

Chebyshev's Theorem to Interpret Standard Deviation

4.5K
Chebyshev’s theorem, also known as Chebyshev’s Inequality, states that the proportion of values of a dataset for K standard deviation is calculated using the equation:
4.5K
Empirical Method to Interpret Standard Deviation01:09

Empirical Method to Interpret Standard Deviation

5.5K
The empirical rule, also known as the three-sigma rule, allows a statistician to interpret the standard deviation in a normally distributed dataset. The rule states that 68% of the data lies within one standard deviation from the mean, 95% lies within two standard deviations from the mean, and 99.7% lies within three standard deviations from the mean. Additionally, this rule is also called the 68-95-99.7 rule.
This rule is used widely in statistics to calculate the proportion of data values...
5.5K
Testing a Claim about Standard Deviation01:19

Testing a Claim about Standard Deviation

2.5K
A complete procedure to test a claim about population standard deviation or population variance is explained here.
The hypothesis testing for the claim of population standard deviation (or variance) requires the data and samples to be random and unbiased. The population distribution also must be normal. There is no specific requirement on the sample size as the estimation is based on the chi-square distribution.
As a first step, the hypothesis (null and alternative) concerning the claim about...
2.5K
Range Rule of Thumb to Interpret Standard Deviation01:13

Range Rule of Thumb to Interpret Standard Deviation

9.3K
The range rule of thumb in statistics helps us calculate a dataset's minimum and maximum values with known standard deviation. This rule is based on the concept that 95% of all values in a dataset lie within two standard deviations from the mean.
For instance, the range rule of thumb can be used to find the tallest and the shortest student in a class, given the mean student height and standard deviation. If the mean student height is 1.6 m and the standard deviation, s is 0.05 m, the height...
9.3K

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相关实验视频

Updated: Sep 19, 2025

A Simple, Robust, and High Throughput Single Molecule Flow Stretching Assay Implementation for Studying Transport of Molecules Along DNA
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A Simple, Robust, and High Throughput Single Molecule Flow Stretching Assay Implementation for Studying Transport of Molecules Along DNA

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iSIM-Sigma:用于分子相似性的高效标准偏差计算

Kenneth Lopez-Perez1, Bill Zhao1, Ramón Alain Miranda-Quintana1

  • 1Department of Chemistry Quantum Theory Project, University of Florida, Gainesville, Florida 32611, United States.

Journal of chemical information and modeling
|June 18, 2025
PubMed
概括

计算分子相似度变异对于化学信息学至关重要,但在计算上昂贵. 这项研究引入了一种更快的准确计算方法,以及对大型分子图书馆的高度准确近似,改善了化学空间探索.

科学领域:

  • 计算化学的计算化学
  • 化学信息学 化学信息学
  • 数据科学数据科学数据科学

背景情况:

  • 分子相似度指标对于化学信息学任务至关重要,例如化学空间探索和子集选择.
  • 计算完整相似度矩阵的方差具有二次复杂度 (O(N^2),这使得它无法用于大型分子图书馆.
  • 对于越来越大的数据集,需要有效的方法来计算相似度差异.

研究的目的:

  • 开发一种计算效率高的方法来计算分子相似性的精确标准偏差.
  • 为了创建一个高度准确的,线性复杂度近似估计分子相似性标准偏差.
  • 在大型分子数据集上实现可扩展的化学信息学分析.

主要方法:

  • 开发了罗素-拉奥 (RR) 和索卡尔-米切纳 (SM) 相似度指数的O(NM^2) 精确计算方法.
  • 提出了基于采样代表分子的线性复杂度O(N) 近似.
  • 将近似方法扩展到其他相似性指数,包括Jaccard-Tanimoto (JT).

主要成果:

  • 与双向方法相比,精确计算方法显著降低了复杂性.
  • 以采样为基础的近似方法可以在50个样本中达到RMSE<0.01,最多为5万个分子.
  • 与拟议方法相比,随机抽样证明不足以准确近似.

更多相关视频

Combined Immunofluorescence and DNA FISH on 3D-preserved Interphase Nuclei to Study Changes in 3D Nuclear Organization
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Combined Immunofluorescence and DNA FISH on 3D-preserved Interphase Nuclei to Study Changes in 3D Nuclear Organization

Published on: February 3, 2013

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Analyzing Melts and Fluids from Ab Initio Molecular Dynamics Simulations with the UMD Package
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Analyzing Melts and Fluids from Ab Initio Molecular Dynamics Simulations with the UMD Package

Published on: September 17, 2021

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相关实验视频

Last Updated: Sep 19, 2025

A Simple, Robust, and High Throughput Single Molecule Flow Stretching Assay Implementation for Studying Transport of Molecules Along DNA
12:05

A Simple, Robust, and High Throughput Single Molecule Flow Stretching Assay Implementation for Studying Transport of Molecules Along DNA

Published on: October 1, 2017

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Combined Immunofluorescence and DNA FISH on 3D-preserved Interphase Nuclei to Study Changes in 3D Nuclear Organization
13:55

Combined Immunofluorescence and DNA FISH on 3D-preserved Interphase Nuclei to Study Changes in 3D Nuclear Organization

Published on: February 3, 2013

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Analyzing Melts and Fluids from Ab Initio Molecular Dynamics Simulations with the UMD Package
06:37

Analyzing Melts and Fluids from Ab Initio Molecular Dynamics Simulations with the UMD Package

Published on: September 17, 2021

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结论:

  • 开发的方法提供了有效和准确的方法来计算分子相似性差异.
  • 线性近似方法使得大分子库上的可扩展和可靠的化学信息学分析成为可能.
  • 这种方法提高了诸如化学空间探索和子集选择等任务的可行性.