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相关概念视频

Cause and Effect01:53

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While variables are sometimes correlated because one does cause the other, it could also be that some other factor, a confounding variable, is actually causing the systematic movement in our variables of interest. For instance, as sales in ice cream increase, so does the overall rate of crime. Is it possible that indulging in your favorite flavor of ice cream could send you on a crime spree? Or, after committing crime do you think you might decide to treat yourself to a cone?
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Bias01:22

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Bias refers to any tendency that prevents a question from being considered unprejudiced. In research, bias occurs when one outcome or answer is selected or encouraged over others in sampling or testing. Bias can occur during any research phase, including study design, data collection, analysis, and publication.
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Stereotypes, Prejudice, and Discrimination02:55

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Humans are very diverse and although we share many similarities, we also have many differences. The social groups we belong to help form our identities (Tajfel, 1974). These differences may be difficult for some people to reconcile, which may lead to prejudice toward people who are different. Prejudice is a negative attitude and feeling toward an individual based solely on one’s membership in a particular social group (Allport, 1954; Brown, 2010). Prejudice is common against people who...
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Confirmation Biases01:31

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The confirmation bias is the tendency to focus on information that confirms our existing beliefs and ignore information that is inconsistent with our expectations. For example, if you think that your professor is not very nice, you notice all of the instances of rude behavior exhibited by the professor while ignoring the countless pleasant interactions he is involved in on a daily basis. Have you ever fallen prey to the confirmation bias, either as the source or target of such bias?
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Strategies for Assessing and Addressing Confounding01:25

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Confounding is a critical issue in epidemiological studies, often leading to misleading conclusions about associations between exposures and outcomes. It occurs when the relationship between the exposure and the outcome is mixed with the effects of other factors that influence the outcome. Given that, addressing confounding is of high importance for drawing accurate inferences in research.
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When we hold a stereotype about a person, we have expectations that he or she will fulfill that stereotype. A self-fulfilling prophecy is an expectation held by a person that alters his or her behavior in a way that tends to make it true. When we hold stereotypes about a person, we tend to treat the person according to our expectations. This treatment can influence the person to act according to our stereotypic expectations, thus confirming our stereotypic beliefs. Research by Rosenthal and...
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Holistic Facial Composite Creation and Subsequent Video Line-up Eyewitness Identification Paradigm
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过度代表性分析存在两个微妙的问题.

Mark Ziemann1,2, Barry Schroeter2, Anusuiya Bora1,2

  • 1Bioinformatics Working Group, Burnet Institute, Melbourne, VIC 3004, Australia.

Bioinformatics advances
|November 14, 2024
PubMed
概括
此摘要是机器生成的。

过度代表性分析 (ORA) 工具可能会与背景基因列表和错误发现率存在问题. 这些影响OMIC数据解释的问题可以通过使用替代工具或方法 (如功能类评分) 来解决.

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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
  • 基因组学就是基因组学.

背景情况:

  • 过度代表性分析 (ORA) 是一种广泛使用的生物信息学方法,用于从'omics数据中解释基因列表.
  • 通过对背景列表进行类别丰富评估,ORA将基因列表与生物功能和主题联系起来.
  • 现有的ORA工具可能会出现"背景问题" (不包括未注释的基因) 和"错误发现率问题" (低估测试).

研究的目的:

  • 在过度代表性分析 (ORA) 工具中证明和量化识别问题的影响.
  • 调查基因组库,列表大小和数据噪声如何影响ORA问题.
  • 提供解决方案,以减轻这些问题在OMIC数据分析.

主要方法:

  • 对真实RNA测序 (RNA-seq) 数据集的分析.
  • 利用模拟的RNA-seq数据进行定量影响评估.
  • 评估不同的ORA套件和替代功能分析方法.

主要成果:

  • 通过使用RNA-seq数据,证明了ORA中的"背景"和"错误发现率"问题的现实影响.
  • 量化了基因组库,基因列表大小和数据噪声对ORA工具性能的影响.
  • 确认问题严重程度因数据集特征和选择的分析方法而异.

结论:

  • 在ORA工具中发现的问题可以显著影响"omics数据"的解释.
  • 像功能类评分这样的替代ORA工具或方法可以减轻这些问题.
  • 一个R/Shiny工具和支持材料可用于应对这些挑战.