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相关概念视频

Quantifying and Rejecting Outliers: The Grubbs Test01:02

Quantifying and Rejecting Outliers: The Grubbs Test

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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...
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Microarrays are high-throughput and relatively inexpensive assays that can be automated to analyze large quantities of data at a time. They are used in genome-wide studies to compare gene or protein expression under two varied conditions, such as healthy and diseased states. Microarrays consist of glass or silica slides on which probe molecules are covalently attached through surface functionalization. Most commonly, the slides are prepared through the chemisorption of silanes to silica...
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Friedman's Two-Way Analysis of Variance by Ranks is a nonparametric test designed to identify differences across multiple test attempts when traditional assumptions of normality and equal variances do not apply. Unlike conventional ANOVA, which requires normally distributed data with equal variances, Friedman's test is ideal for ordinal or non-normally distributed data, making it particularly useful for analyzing dependent samples, such as matched subjects over time or repeated measures...
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When the fitness of a trait is influenced by how common it is (i.e., its frequency) relative to different traits within a population, this is referred to as frequency-dependent selection. Frequency-dependent selection may occur between species or within a single species. This type of selection can either be positive—with more common phenotypes having higher fitness—or negative, with rarer phenotypes conferring increased fitness.
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Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances
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使用自适应图形学习和等级约束的双重规范化子空间学习:对基因表达微阵列数据集的无监督特征选择.

Amir Moslemi1, Arash Ahmadian2

  • 1Imaging Research and Physical Sciences, Sunnybrook Health Sciences Centre, Toronto, Ontario, Canada.

Computers in biology and medicine
|November 11, 2023
PubMed
概括
此摘要是机器生成的。

这项研究为高维基因数据引入了一种新的无监督特征选择方法. 该技术有效地减少了冗余功能,提高了未标记数据集的分析准确性.

关键词:
基因选择 基因选择国内产品规范 国内产品规范保护当地信息的保护.微阵列数据集是一个微阵列数据集.非负矩阵因子化的因子化排名约束 排名约束没有监督的特征选择选择.

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科学领域:

  • 生物信息学是一种生物信息学.
  • 计算生物学 计算生物学
  • 机器学习 机器学习

背景情况:

  • 在基因表达中常见的高维数据,提出了诸如维度的诅咒之类的挑战.
  • 微阵列数据集往往具有比样本更多的特征,导致错误的问题.
  • 冗余功能降低学习算法性能,增加计算时间.

研究的目的:

  • 为未标记的数据开发一个强大的无监督特征选择方法.
  • 为了解决基因表达分析中维度的诅咒.
  • 通过最大限度地提高相关性和最大限度地减少冗余性来选择歧视性特征子集.

主要方法:

  • 非负矩阵分解 (NMF) 用于数据分解.
  • 用特征和表示矩阵的内部乘积规范进行双规范化.
  • 适应性结构学习以保存本地信息.
  • 使用Schatten-p规范的等级约束.

主要成果:

  • 拟议的方法有效地抛弃了冗余的功能,同时保留了信息性的功能.
  • 在六个基准微阵列数据集上表现出卓越的性能.
  • 超过了八种最先进的无监督特征选择技术.

结论:

  • 这种新强大的无监督特征选择技术对于高维基因数据是有效的.
  • 实现了更好的聚类准确性和规范化的相互信息.
  • 为分析未标记的生物数据提供了一种有价值的方法.