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相关概念视频

Multiple Allele Traits01:49

Multiple Allele Traits

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When more than one gene is responsible for a given phenotype, the trait is considered polygenic. Human height is a polygenic trait. Studies have uncovered hundreds of loci that influence height, and there are believed to be many more. Due to the high number of genes involved, as well as environmental and nutritional factors, height varies significantly within a given population. The distribution of height forms a bell-shaped curve, with relatively few individuals in the population at the...
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Pleiotropy is the phenomenon in which a single gene impacts multiple, seemingly unrelated phenotypic traits. For example, defects in the SOX10 gene cause Waardenburg Syndrome Type 4, or WS4, which can cause defects in pigmentation, hearing impairments, and an absence of intestinal contractions necessary for elimination. This diversity of phenotypes results from the expression pattern of SOX10 in early embryonic and fetal development. SOX10 is found in neural crest cells that form melanocytes,...
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Although the genetic makeup of an organism plays a major role in determining the phenotype, there are also several environmental factors, such as temperature, oxygen availability, presence of mutagens, that can alter an organism’s phenotype.
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Although Mendel chose seven unrelated traits in peas to study gene segregation, most traits involve multiple gene interactions that create a spectrum of phenotypes. When the interaction of various genes or alleles at different locations influences a phenotype, this is called epistasis. Epistasis often involves one gene masking or interfering with the expression of another (antagonistic epistasis). Epistasis often occurs when different genes are part of the same biochemical pathway. The...
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When analyzing elongated structures like bars subjected to uniformly distributed loads, it is essential to understand the transformation of plane strain when coordinate axes are rotated. This transformation helps to assess how material deformation characteristics vary with orientation, which is crucial in materials science and structural engineering.
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组合转换可以合理地将表型相关值引入稀疏特征中.

George I Austin1,2, Tal Korem2,3

  • 1Department of Biomedical Informatics, Columbia University Irving Medical, New York, New York, USA.

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概括
此摘要是机器生成的。

微生物组的稀有特征在转变后变得与表型相关,并不一定表明信息泄露. 我们的反例表明,这些变化可以来自有效的数据处理,而不仅仅是有缺陷的管道.

关键词:
组合数据分析数据分析.归算是指指责一个人.机器学习是机器学习.微生物组是一个微生物组.

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科学领域:

  • 微生物组生物信息学
  • 计算生物学是一种计算生物学.
  • 对高维数据的统计分析.

背景情况:

  • 吉哈维等人. 吉哈维等人. 质疑Poore等人对瘤微生物组分析的有效性,引用稀疏特征 (读数很少的基因) 在批次校正后变得与表型相关.
  • 这种批评意味着这样的转变表明信息泄露,并使分析无效,影响"癌症基因组图谱" (TCGA) 微生物组研究和更广泛的微生物组研究的解释.

研究的目的:

  • 调查数据转换后出现表型相关的稀疏特征是否必然意味着信息泄露或微生物组分析中的处理错误.
  • 提供反例,证明这样的观察可以从有效的统计转换中得到,从而挑战广泛的无效性索赔.

主要方法:

  • 检查了中心日志比率 (CLR) 转换,这是组成微生物组数据的常用方法,并注意到其样本智能的性质和与批次校正方法的相似之处.
  • 利用合成和阴道微生物组数据集来证明CLR转换如何,加上归算策略,可以将稀疏特征与几何平均值联系起来,因此,表型.
  • 重新分析了Gihawi等人强调的特定特征. 为了证明观察到的现象即使在CLR转换后也可能发生,作为信息泄露索赔的反例.

主要成果:

  • 中心日志比率 (CLR) 转换是一个样本智能的操作,不能本质上泄露信息或使下游分析无效.
  • 在CLR转换的数据中,对于零或缺失值的常用归算方法可以导致转换的特征与样本的几何平均值之间的关联.
  • 当几何平均值与表型相关时,稀疏和CLR转换的特征也与表型相关,这种现象在合成和真实微生物组数据中观察到.
  • 重新分析证实,在CLR转换后,稀有特征变得与表型相关可以发生,这驳斥了这一说法,即仅此观察就表明信息泄露.

结论:

  • 在数据转换后出现与表型相关的稀疏特征并不是足够的证据来声称机器学习管道中的信息泄漏.
  • 像CLR这样的样本智能转换可以在不人工膨胀性能的情况下产生这种关联,这表明最初的批评可能过于广泛.
  • 解释微生物组数据中的个体特征需要谨慎,因为数据的多变量性质以及转换和批量校正方法的影响.