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相关概念视频

Bar Graph01:07

Bar Graph

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A bar graph is also called a bar chart and consists of bars that are separated from each other. It either uses horizontal or vertical bars to show comparisons among categories. The bars can be rectangles, or they can be rectangular boxes (used in three-dimensional plots). One axis of the graph represents the specific categories being compared, and the other axis shows a discrete value. In this graph, the length of the bar for each category is proportional to the number or percent of individuals...
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Multiple Bar Graph01:07

Multiple Bar Graph

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As the name suggests, a multiple bar graph is the same as a bar graph but has multiple bars to depict relationships between different data values. One can include as many parameters as possible. However, each parameter must have the same unit of measurement.
Each bar or column in the multiple bar graph represents a data value. These graphs are used primarily in interrelating two or more sets of data. The categories of different kinds of data are listed along the horizontal or x-axis, whereas...
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5-Number Summary01:04

5-Number Summary

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In a dataset, the 5-number summary includes the minimum data value, the data value of the first quartile, the median data value or data value of the second quartile, the data value of the third quartile, and the maximum data value. These 5 data values can be visualized as a box and whisker plot.
In a box plot, the minimum and maximum data values represent the lower and upper whiskers in the graph, and the median is designated as the center of the box in the chart. The first quartile and third...
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Boxplot01:12

Boxplot

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Box plots (also called box-and-whisker plots or box-whisker plots) give an excellent graphical image of the concentration of the data. They also show how far the extreme values are from most data. A box plot is constructed from five values: the minimum value, the first quartile, the median, the third quartile, and the maximum value. We use these values to compare how close other data values are to them. To construct a box plot, use a horizontal or vertical number line and a rectangular box. The...
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Contingency Table01:29

Contingency Table

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A contingency table provides a way of portraying data that can facilitate calculating probabilities. It is a method of displaying a frequency distribution as a table with rows and columns to show how two variables may be dependent (contingent) upon each other; The table helps determine conditional probabilities quite quickly and can help systematically organize, analyze and quantify data. The table displays sample values concerning two variables that may be dependent or contingent on one...
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pV-Diagrams01:18

pV-Diagrams

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The pV diagram, which is a graph of pressure versus volume of the gas under study, is helpful in describing certain aspects of the substance. When the substance behaves like an ideal gas, the ideal gas equation describes the relationship between its pressure and volume. On a pV diagram, it is common to plot an isotherm, which is a curve showing p as a function of V with the number of molecules and the temperature fixed. Then, for an ideal gas, the product of the pressure of the gas and its...
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相关实验视频

Updated: Jun 24, 2025

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances
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Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances

Published on: October 11, 2018

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量化可视化二分数据集的数据集

Tal Einav1, Yuehaw Khoo2, Amit Singer3

  • 1Divisions of Computational Biology and Basic Sciences, Fred Hutchinson Cancer Center, Seattle, Washington 98109, USA.

Physical review. X
|June 4, 2024
PubMed
概括
此摘要是机器生成的。

这项研究引入了修改后的算法来解决二分位本地化问题,使得像抗体-病毒相互作用这样的两类数据集中复杂关系的映射成为可能. 这些发现从地方测量提供了更清晰的全球图像.

关键词:
生物物理 生物物理计算物理 计算物理

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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.
  • 系统生物学 系统生物学

背景情况:

  • 分析大规模实验数据在将对对测量整合到全球理解中提出了挑战.
  • 经典的本地化问题将本地交互映射出来,以揭示系统结构,但双边数据需要专门的方法.

研究的目的:

  • 为了解决双边本地化问题,其中距离数据仅存在于两个不同的类别的条目之间.
  • 适应和评估现有的局部化算法对双边数据集,考虑到噪音,异常值和缺失的数据.

主要方法:

  • 修改已建立的本地化算法以处理双边数据结构.
  • 在各种数据缺陷下对算法性能进行评估,包括噪声,异常值和部分观察.
  • 精细算法的应用对抗体-病毒中和数据.

主要成果:

  • 针对病毒的抗体行为特征的基础集的开发.
  • 通过特定抗体对某些病毒的强烈抑制和对其他病毒的弱抑制之间的权衡的正式化.
  • 在抗体组合中对退化的行为进行量化.

结论:

  • 修改后的双边本地化算法有效地映射出复杂的交互场景.
  • 通过双边定位来理解抗体行为,可以揭示组合中的协同作用或对抗作用.
  • 这种方法为解释大规模的双重生物数据提供了一个框架.