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相关概念视频

Sampling Plans01:23

Sampling Plans

169
Sampling is a crucial step in analytical chemistry, allowing researchers to collect representative data from a large population. Common sampling methods include random, judgmental, systematic, stratified, and cluster sampling.
Random sampling is a method where each member of the population has an equal chance of being selected for the sample. It involves selecting individuals randomly, often using random number generators or lottery-type methods. For example, when analyzing the properties of a...
169
Stratified Sampling Method01:16

Stratified Sampling Method

11.7K
Sampling is a technique to select a portion (or subset) of the larger population and study that portion (the sample) to gain information about the population. The sampling method ensures that samples are drawn without bias and accurately represent the population. Because measuring the entire population in a study is not practical, researchers use samples to represent the population of interest.
To choose a stratified sample, divide the population into groups called strata and then take a...
11.7K
Quantifying and Rejecting Outliers: The Grubbs Test01:02

Quantifying and Rejecting Outliers: The Grubbs Test

1.5K
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...
1.5K
Detection of Gross Error: The Q Test01:00

Detection of Gross Error: The Q Test

5.6K
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...
5.6K
Qualitative Analysis01:10

Qualitative Analysis

227
Qualitative analysis is the process of identifying elements, ions, or compounds in an unknown sample. It is the first and most fundamental type of analysis based on the hierarchy of analytical goals. This hierarchy is significant as it provides a structured approach to scientific research, with qualitative analysis serving as the initial step, providing essential information before moving on to quantitative or other forms of analysis.
There are two main approaches to qualitative analysis:...
227
5-Number Summary01:04

5-Number Summary

4.2K
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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相关实验视频

Updated: Jun 7, 2025

Quantification of Orofacial Phenotypes in Xenopus
09:26

Quantification of Orofacial Phenotypes in Xenopus

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基于数量目标的子组识别.

Yan Sun1, A S Hedayat2

  • 1AbbVie, North Chicago, Illinois, USA.

Pharmaceutical statistics
|November 17, 2024
PubMed
概括
此摘要是机器生成的。

精准医学通过分组识别来推进药物开发. 我们的新方法 squant 创建了用于稳定,可解释的签名和错误发现率控制的人工试验.

关键词:
生物标志物生物标志物精准医学是一门精准医学.小组的标识子组的标识

更多相关视频

A Strategy for Sensitive, Large Scale Quantitative Metabolomics
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Large-scale Reconstructions and Independent, Unbiased Clustering Based on Morphological Metrics to Classify Neurons in Selective Populations
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相关实验视频

Last Updated: Jun 7, 2025

Quantification of Orofacial Phenotypes in Xenopus
09:26

Quantification of Orofacial Phenotypes in Xenopus

Published on: November 6, 2014

9.7K
A Strategy for Sensitive, Large Scale Quantitative Metabolomics
14:18

A Strategy for Sensitive, Large Scale Quantitative Metabolomics

Published on: May 27, 2014

20.9K
Large-scale Reconstructions and Independent, Unbiased Clustering Based on Morphological Metrics to Classify Neurons in Selective Populations
12:27

Large-scale Reconstructions and Independent, Unbiased Clustering Based on Morphological Metrics to Classify Neurons in Selective Populations

Published on: February 15, 2017

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科学领域:

  • 生物统计学 生物统计学
  • 药物基因组学 药物基因组学
  • 计算生物学 计算生物学

背景情况:

  • 精准医学旨在为个别患者量身定制治疗,但识别相关子组是具有挑战性的.
  • 确定子组对于优化药物开发和治疗疗效至关重要.

研究的目的:

  • 介绍 squant,一个新的端到端计算解决方案用于药物开发中的子组识别.
  • 提供一种灵活和可解释的方法,以发现对治疗有不同的反应的患者子组.

主要方法:

  • 置方法将研究转化为人工 1:1 随机试验.
  • 它采用灵活的目标功能,并确保稳定,可解释的签名.
  • 错误发现率 (FDR) 控制嵌入了该方法.

主要成果:

  • 模拟演示了 squant 方法的强大性能.
  • 该方法在现实数据示例中成功识别了相关的子组.
  • 生成的签名既稳定又可解释.

结论:

  • squant提供了一种强大而实用的工具,用于精密医学中的子组识别.
  • 该方法通过发现治疗特定的患者子组,促进了更有效的药物开发.
  • 可解释性和FDR控制增强了已识别的签名的临床实用性.