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Updated: Jun 13, 2025

Inherent Dynamics Visualizer, an Interactive Application for Evaluating and Visualizing Outputs from a Gene Regulatory Network Inference Pipeline
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Inherent Dynamics Visualizer, an Interactive Application for Evaluating and Visualizing Outputs from a Gene Regulatory Network Inference Pipeline

Published on: December 7, 2021

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在高维线性模型中不确定性量化,这些模型包含图形结构,并用于基因组分析.

Xiangyong Tan1, Xiao Zhang2, Yuehua Cui3

  • 1School of Statistics and Data Science, Jiangxi University of Finance and Economics, Nanchang 330013, China.

Bioinformatics (Oxford, England)
|September 10, 2024
PubMed
概括

这项研究引入了一种新的图形受约束的分散式LASSO (GCDL) 方法,用于在高维模型中量化基因不确定性. 该GCDL估计器提供准确的置信区间和P值,即使与高度相关的预测器.

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Author Spotlight: Impact of Intergenic Interactions on Disease-Identifying Dark Biomarkers03:37

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Uncertainty: Overview00:59

Uncertainty: Overview

In analytical chemistry, we often perform repetitive measurements to detect and minimize inaccuracies caused by both determinate and indeterminate errors. Despite the cares we take, the presence of random errors means that repeated measurements almost never have exactly the same magnitude. The collective difference between these measurements - observed values - and the estimated or expected value is called uncertainty. Uncertainty is conventionally written after the estimated or expected value.
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This lesson introduces two critical methods in pharmacokinetics, the Wagner-Nelson and Loo-Riegelman methods, used for estimating the absorption rate constant (ka) for drugs administered via non-intravenous routes. The Wagner-Nelson method relates ka to the plasma concentration derived from the slope of a semilog percent unabsorbed time plot. However, it is limited to drugs with one-compartment kinetics and can be impacted by factors like gastrointestinal motility or enzymatic degradation.
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