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Uncertainty: Overview00:59

Uncertainty: Overview

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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.
523
Propagation of Uncertainty from Systematic Error01:10

Propagation of Uncertainty from Systematic Error

482
The atomic mass of an element varies due to the relative ratio of its isotopes. A sample's relative proportion of oxygen isotopes influences its average atomic mass. For instance, if we were to measure the atomic mass of oxygen from a sample, the mass would be a weighted average of the isotopic masses of oxygen in that sample. Since a single sample is not likely to perfectly reflect the true atomic mass of oxygen for all the molecules of oxygen on Earth, the mass we obtain from this...
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Propagation of Uncertainty from Random Error00:59

Propagation of Uncertainty from Random Error

652
An experiment often consists of more than a single step. In this case, measurements at each step give rise to uncertainty. Because the measurements occur in successive steps, the uncertainty in one step necessarily contributes to that in the subsequent step. As we perform statistical analysis on these types of experiments, we must learn to account for the propagation of uncertainty from one step to the next. The propagation of uncertainty depends on the type of arithmetic operation performed on...
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Uncertainty: Confidence Intervals00:54

Uncertainty: Confidence Intervals

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The confidence interval is the range of values around the mean that contains the true mean. It is expressed as a probability percentage. The interpretation of a 95% confidence interval, for instance, is that the statistician is 95% confident that the true mean falls within the interval. The upper and lower limits of this range are known as confidence limits. The confidence limits for the true mean are estimated from the sample's mean, the standard deviation, and the statistical factor...
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Improving Translational Accuracy02:07

Improving Translational Accuracy

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Base complementarity between the three base pairs of mRNA codon and the tRNA anticodon is not a failsafe mechanism. Inaccuracies can range from a single mismatch to no correct base pairing at all. The free energy difference between the correct and nearly correct base pairs can be as small as 3 kcal/ mol. With complementarity being the only proofreading step, the estimated error frequency would be one wrong amino acid in every 100 amino acids incorporated. However, error frequencies observed in...
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Uncertainty in Measurement: Accuracy and Precision03:37

Uncertainty in Measurement: Accuracy and Precision

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Scientists typically make repeated measurements of a quantity to ensure the quality of their findings and to evaluate both the precision and the accuracy of their results. Measurements are said to be precise if they yield very similar results when repeated in the same manner. A measurement is considered accurate if it yields a result that is very close to the true or the accepted value. Precise values agree with each other; accurate values agree with a true value. 
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相关实验视频

Updated: Jun 6, 2025

A Virtual Machine Platform for Non-Computer Professionals for Using Deep Learning to Classify Biological Sequences of Metagenomic Data
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不确定性意识的基因组深度学习与知识蒸.

Jessica Zhou1, Kaeli Rizzo1, Ziqi Tang1,2

  • 1Simons Center for Quantitative Biology, Cold Spring Harbor Laboratory, NY, USA.

bioRxiv : the preprint server for biology
|November 28, 2024
PubMed
概括
此摘要是机器生成的。

基因组学中的深度神经网络 (DNN) 得到了DEGU (基因组不确定性意识模型的蒸合体) 的改进. 这种方法通过结合集体学习和知识蒸来提高预测可靠性和可解释性,以进行强大的基因组不确定性建模.

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科学领域:

  • 基因组学就是基因组学.
  • 机器学习 机器学习
  • 计算生物学 计算生物学

背景情况:

  • 深度神经网络 (DNN) 对调控基因组学预测具有强大作用.
  • 在确保DNN预测可靠性和可解释性方面存在挑战.
  • 了解模型决策对于生物见解至关重要.

研究的目的:

  • 引入DEGU (基因组不确定性意识模型的蒸合体),以提高DNN的稳定性和可解释性.
  • 整合集体学习和知识蒸,以改善基因组预测.
  • 为基因组学中可靠的深度学习应用提供校准的不确定性估计.

主要方法:

  • DEGU将多个DNN集结成一个单一的模型.
  • 捕获平均预测和可变性 (认识体系不确定性).
  • 包括一个辅助任务来估计基于数据的 (随机) 不确定性.

主要成果:

  • DEGU模型在单个模型中继承了整体性能优势.
  • 改进了对分布外序列的概括.
  • 通过归因分析对cis-regulatory机制进行一致的解释.
  • 校准的不确定性估计与合规预测覆盖率的保证.

结论:

  • DEGU能够在基因组学中实现强大的和可解释的深度学习.
  • 提高预测模型的可靠性和可解释性.
  • 促进人工智能在生物研究中的可靠应用.