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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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Pleiotropy01:33

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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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X-linked Traits01:19

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In most mammalian species, females have two X sex chromosomes and males have an X and Y. As a result, mutations on the X chromosome in females may be masked by the presence of a normal allele on the second X. In contrast, a mutation on the X chromosome in males more often causes observable biological defects, as there is no normal X to compensate. Trait variations arising from mutations on the X chromosome are called “X-linked”.
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Background and Environment Affect Phenotype02:27

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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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Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data
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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, NY, USA.

bioRxiv : the preprint server for biology
|January 20, 2025
PubMed
概括
此摘要是机器生成的。

瘤微生物组数据中的稀有特征可能会因数据转换而与表型相关,例如中心日志比率 (CLR),不一定表明机器学习管道问题. 这一发现挑战了微生物组分析中信息泄露的说法.

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科学领域:

  • 微生物组研究 微生物组研究
  • 生物信息学是一种生物信息学.
  • 机器学习 机器学习

背景情况:

  • 最近的论点表明,如果在批次校正后稀疏特征成为表型相关的,与瘤相关的微生物组数据分析是无效的.
  • 这引发了人们对处理或机器学习管道中的潜在信息泄露的担忧.

研究的目的:

  • 调查稀有特征是否与表型相关,必然表明微生物组数据处理或机器学习管道存在问题.
  • 证明样本智能的转换可以在没有信息泄露的情况下创建这样的关联.

主要方法:

  • 利用中心日志比率 (CLR) 转换,这是对组成微生物组数据的常用方法.
  • 分析了合成和阴道微生物组数据集.
  • 重新分析了Gihawi等人先前认为有问题的特征.

主要成果:

  • 证明了CLR转换,一个样本智能的操作,可以导致最初稀疏的特征与表型相关联.
  • 这种关联发生在CLR中使用的几何平均值与表型相关时,特别是对于零值的常见归算策略.
  • 证明了在CLR中观察到的这种现象,可以作为必要信息泄露的主张的反例.

结论:

  • 在像CLR这样的样本智能转换后,表型相关的稀疏特征的出现并不能独立地证明机器学习管道中的信息泄漏.
  • 这种观察可以来自转换的性质和数据特征,不一定来自管道工件.
  • 强调需要谨慎解释多变量微生物组数据中的个体特征.