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相关概念视频

Mechanistic Models: Compartment Models in Individual and Population Analysis01:23

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Mechanistic models are utilized in individual analysis using single-source data, but imperfections arise due to data collection errors, preventing perfect prediction of observed data. The mathematical equation involves known values (Xi), observed concentrations (Ci), measurement errors (εi), model parameters (ϕj), and the related function (ƒi) for i number of values. Different least-squares metrics quantify differences between predicted and observed values. The ordinary least...
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Mechanistic models play a crucial role in algorithms for numerical problem-solving, particularly in nonlinear mixed effects modeling (NMEM). These models aim to minimize specific objective functions by evaluating various parameter estimates, leading to the development of systematic algorithms. In some cases, linearization techniques approximate the model using linear equations.
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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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Candidate Gene Testing in Clinical Cohort Studies with Multiplexed Genotyping and Mass Spectrometry
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高维度,取决于结果的缺失数据问题:人类位置模型

Lars Leonardus Joannes van der Burg1, Hein Putter1, Henning Baldauf2

  • 1Biomedical Data Sciences, LUMC, Leiden, The Netherlands.

Statistical methods in medical research
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PubMed
概括
此摘要是机器生成的。

将结果模型纳入KIR双型缺失数据归算中可以引入偏差. 没有结果建模的基线预期最大化算法通常表现更好或可比,特别是在高维生物数据中.

关键词:
基因基因基因基因基因基因基因基因基因基因基因基因基因基因缺少的数据数据.预期最大化算法是指期望最大化算法.复原型重建复原型的重建多重的归算是多重的归算.取决于结果的归算.

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科学领域:

  • 遗传学 是一个遗传学.
  • 生物统计学 生物统计学
  • 计算生物学 计算生物学

背景情况:

  • 缺失的数据在高维生物数据集中普遍存在.
  • 输入和预期最大化 (EM) 算法用于数据重建.
  • 将回归模型集成到归算中可能会减少回归系数偏差.

研究的目的:

  • 评估基于结果的EM算法,用于重建缺少数据的KIR双型.
  • 将结合高维回归模型的策略与基线EM算法进行比较.

主要方法:

  • 扩展了之前提出的EM算法,包括一个高维回归模型.
  • 评估了三种策略:仅对等位基预测者,对等位基预测者与类型选择,并处罚回归.
  • 通过模拟将这些策略与没有结果模型的基线EM算法进行了比较.

主要成果:

  • 基于结果的EM算法在效果大小和缺失的极端场景中表现优于基线.
  • 在大多数情况下,基线EM算法的性能优于或相似.
  • 包含一个结果模型可能会引入有害影响和偏见.

结论:

  • 基于结果的缺失数据模型在高维设置中需要仔细应用.
  • 这些模型可能会导致偏见的结果,特别是在重建KIR双型时.
  • 没有结果建模的基线EM算法通常是一种更强大的方法.