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相关概念视频

Accuracy, limits, and approximation01:28

Accuracy, limits, and approximation

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Accuracy, limits, and approximations are common in many fields, especially in engineering calculations. These concepts are imperative for ensuring that a given value is as close as possible to its true value.
Accuracy is defined as the closeness of the measured value to the true or actual value. In engineering mechanics, repeated measurements are taken during theoretical or experimental analyses to ensure that the result is precise and accurate.
The accuracy of any solution is based on the...
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Estimation of the Physical Quantities01:05

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On many occasions, physicists, other scientists, and engineers need to make estimates of a particular quantity. These are sometimes referred to as guesstimates, order-of-magnitude approximations, back-of-the-envelope calculations, or Fermi calculations. The physicist Enrico Fermi was famous for his ability to estimate various kinds of data with surprising precision. Estimating does not mean guessing a number or a formula at random. Instead, estimation means using prior experience and sound...
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Fineness Modulus01:19

Fineness Modulus

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The fineness modulus (FM) of aggregate is a numerical index that measures the coarseness or fineness of the particles. It is calculated by adding the cumulative percentages of aggregate retained on each of a specified series of sieves and dividing the sum by 100.
Consider performing sieve analysis on sand through a set of ASTM sieves. The weight of aggregate retained in each sieve and pan placed at the bottom is recorded, as given in Column B of Table 1.
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One-Way ANOVA: Equal Sample Sizes01:15

One-Way ANOVA: Equal Sample Sizes

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One-Way ANOVA can be performed on three or more samples with equal or unequal sample sizes. When one-way ANOVA is performed on two datasets with samples of equal sizes, it can be easily observed that the computed F statistic is highly sensitive to the sample mean.
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One-Way ANOVA: Unequal Sample Sizes01:15

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One-way ANOVA can be performed on three or more samples of unequal sizes. However, calculations get complicated when sample sizes are not always the same. So, while performing ANOVA with unequal samples size, the following equation is used:
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Identifying Statistically Significant Differences: The F-Test01:14

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The F-test is used to compare two sample variances to each other or compare the sample variance to the population variance. It is used to decide whether an indeterminate error can explain the difference in their values. The underlying assumptions that allow the use of the F-test include the data set or sets are normally distributed, and the data sets are independent of each other. The test statistic F is calculated by dividing one variance by another. In other words, the square of one standard...
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使用FracMinHash估计相似性和距离.

Mahmudur Rahman Hera1, David Koslicki2,3,4

  • 1School of Electrical Engineering and Computer Science, Pennsylvania State University, University Park, USA. mbr5797@psu.edu.

Algorithms for molecular biology : AMB
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概括

本研究介绍了FracMinHash草图的理论框架,用于估计基因组数据中的各种相似度指标. 一个新的工具, frac-kmc,为准确的相似性分析提供快速并行草图生成.

关键词:
在FracMinHash中使用FracMinHash.哈希希 (Hashing) 是一个叫做哈希希的公司.敏哈什 (Min-Hash) 是一个小镇.相似之处 相似之处草图绘制 草图绘制理论上的理论理论.这里是K-MER.

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科学领域:

  • 计算生物学 计算生物学
  • 生物信息学是一种生物信息学.
  • 基因组学就是基因组学.

背景情况:

  • 基因组和元基因组数据分析需要可扩展的计算模型.
  • 素描技术,特别是FracMinHash,对于大规模的生物数据分析是有价值的.
  • 虽然FracMinHash是为Jaccard和封闭指数建立的,但对于其他指标存在理论上的差距.

研究的目的:

  • 使用FracMinHash草图开发一个用于估计相似性/距离指标的理论框架.
  • 建立声音估计的条件,并推准确度的参数.
  • 介绍一个新的,高效的FracMinHash素描生成器.

主要方法:

  • 开发了基于FracMinHash的指标估计的理论框架.
  • 确定了条件和规模因子,以准确估计.
  • 实现了 frac-kmc,一个并行的 FracMinHash 草图生成工具.

主要成果:

  • 验证的理论发现与实验证据.
  • frac-kmc被证明是最快的FracMinHash草图生成器.
  • 通过使用 frac-kmc 在真实基因组数据上实现了准确和精确的同位素相似性估计.

结论:

  • 该理论框架允许从FracMinHash草图中对各种指标进行合理估计.
  • frac-kmc为草图生成提供了显著的加速和并行化.
  • 这项工作增强了FracMinHash对于大规模基因组数据分析的实用性.