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相关概念视频

Applications of Normal Distribution01:22

Applications of Normal Distribution

5.0K
The normal distribution is a useful statistical tool. One of its practical applications is determining the door height after considering the normal distribution of heights of persons, such that many can pass through it easily without striking their heads. The normal distribution can also determine the probability of a person having a height less than a specific height.
The heights of 15 to 18-year-old males from Chile from 1984 to 1985 followed a normal distribution. The mean height is 172.36...
5.0K
Central Limit Theorem01:14

Central Limit Theorem

14.7K
The central limit theorem, abbreviated as clt, is one of the most powerful and useful ideas in all of statistics. The central limit theorem for sample means says that if you repeatedly draw samples of a given size and calculate their means, and create a histogram of those means, then the resulting histogram will tend to have an approximate normal bell shape. In other words, as sample sizes increase, the distribution of means follows the normal distribution more closely.
The sample size, n, that...
14.7K
Sample Proportion and Population Proportion01:20

Sample Proportion and Population Proportion

5.3K
Collecting samples or responses from an entire population takes significant time and effort, so a researcher collects responses from only a sample of that population. Suppose a study needs to collect information about a specific mobile application. After sample collection, the researcher analyzes the data and discovers that most individuals in the sample use that specific mobile application. The sample proportion measures the number of individuals in a sample who either use or don't use the...
5.3K
Normal Distribution01:11

Normal Distribution

10.8K
The normal, a continuous distribution, is the most important of all the distributions. Its graph is a bell-shaped symmetrical curve, which is observed in almost all disciplines. Some of these include psychology, business, economics, the sciences, nursing, and, of course, mathematics. Some instructors may use the normal distribution to help determine students’ grades. Most IQ scores are normally distributed. Often real-estate prices fit a normal distribution. The normal distribution is...
10.8K
Distributions to Estimate Population Parameter01:26

Distributions to Estimate Population Parameter

4.1K
The accurate values of population parameters such as population proportion, population mean, and population standard deviation (or variance) are usually unknown. These are fixed values that can only be estimated from the data collected from the samples. The estimates of each of these parameters are sample proportion, the sample mean, and sample standard deviation (or variance). To obtain the values of these sample statistics, data are required that have particular distribution and central...
4.1K
Testing a Claim about Population Proportion01:24

Testing a Claim about Population Proportion

3.3K
A complete procedure for testing a claim about a population proportion is provided here.
There are two methods of testing a claim about a population proportion: (1) Using the sample proportion from the data where a binomial distribution is approximated to the normal distribution and (2) Using the binomial probabilities calculated from the data.
The first method uses normal distribution as an approximation to the binomial distribution. The requirements are as follows: sample size is large...
3.3K

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相关实验视频

Updated: Jul 1, 2025

Large-Scale Multi-Omics Genome-Wide Association Studies Mo-GWAS: Guidelines for Sample Preparation and Normalization
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基于比例的规范化在机器学习应用中优于构成性数据转换.

Aaron Yerke1,2, Daisy Fry Brumit1, Anthony A Fodor3

  • 1Department of Bioinformatics and Genomics, Bioinformatics Building, UNC Charlotte, The University of North Carolina, Charlotte 9331 Robert D. Snyder Rd, Charlotte, USA.

Microbiome
|March 5, 2024
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概括
此摘要是机器生成的。

对于微生物群的机器学习,简单的相对丰度转换往往优于复杂的组成意识方法. 尽量减少转换的复杂性,同时纠正读取深度是一个推的策略.

关键词:
组合数据是指组成的数据.高通量核酸测序的测序机器学习 机器学习转基因组学是指转基因组学.规范化 规范化 规范化菲尔尔尔 (Philler) 是一个哲学家.随机的森林随机的森林统计数据的解释变化 转化 转化 转化

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

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O-cresol Concentration Online Measurement Based On Near Infrared Spectroscopy Via Partial Least Square Regression
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O-cresol Concentration Online Measurement Based On Near Infrared Spectroscopy Via Partial Least Square Regression

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相关实验视频

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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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科学领域:

  • 微生物组生物信息学
  • 机器学习在生物学中的应用
  • 数据预处理技术数据的预处理技术.

背景情况:

  • 规范化对于微生物组机器学习解决方案至关重要.
  • 存在许多规范化方案,影响分析结果.
  • 组合数据分析方法越来越多地用于微生物组数据.

研究的目的:

  • 在机器学习中评估微生物组数据的各种规范化技术的性能.
  • 为了比较构成意识转换 (alr,clr,ilr/PhILR) 与构成天真和基于相对丰度的方法.
  • 确定微生物组机器学习的最佳预处理策略.

主要方法:

  • 利用了四个公开的微生物组数据集在片序列变体 (ASV) 层面.
  • 应用了组成意识 (alr,clr,ilr/PhILR) 和天真 (原始计数,比例,Hellinger,lognorm) 的变换.
  • 使用随机森林机器学习算法,使用65个元数据变量进行评估.

主要成果:

  • 均衡树的预处理步骤对性能影响最小.
  • 构成意识转换 (alr,clr,ilr/PhILR) 的性能与天真转换相似或更差.
  • 基于相对丰度的转换显示了比大多数其他方法的统计学上显著的性能改善.

结论:

  • 更简单的转换,比如那些基于相对丰富的转换,往往是微生物组机器学习的首选.
  • 尽量减少转换的复杂性,同时解决读取深度是一个实际的策略.
  • 复杂的组合校正可能并不总是比更简单的方法产生更优异的结果.