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相关概念视频

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.
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Propagation of Uncertainty from Systematic Error01:10

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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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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

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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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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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不确定性意识的基因组深度学习与知识蒸.

Jessica Zhou1, Kaeli Rizzo1, Trevor Christensen1

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

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概括
此摘要是机器生成的。

我们开发了DEGU (Distilling Ensembles for Genomic Uncertainty-aware models),一种新的方法,可以提高深度神经网络的可靠性和基因组学中的可解释性. DEGU提高了预测准确度,并为监管基因组学任务提供可靠的不确定性估计.

关键词:
计算生物学和生物信息学计算模型是计算模型.

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科学领域:

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

背景情况:

  • 深度神经网络 (DNN) 在监管基因组学方面表现有前途,但在预测可靠性和解释性方面面临挑战.
  • 了解驱动DNN预测的因素对于生物见解和模型验证至关重要.

研究的目的:

  • 引入DEGU (Distilling Ensembles for Genomic Uncertainty-aware models),一种增强DNN稳定性和可解释性的方法. 这种方法可以提高DNN的稳定性和可解释性.
  • 通过深度学习为监管基因组学任务提供可靠和可解释的预测.

主要方法:

  • DEGU集成集体学习和知识蒸,以创建一个单一的,不确定性意识的DNN.
  • 它从一个集合中捕获了预测共识和可变性 (认识体系不确定性).
  • 一个可选的任务模型从实验复制的数据变化 (选择性不确定性).

主要成果:

  • 经过DEGU训练的模型在单个模型中实现了整体性能,从而改善了对分布外序列的概括性.
  • 归因分析揭示了对cis-regulatory机制的更一致的解释.
  • 模型提供校准的不确定性估计与合规预测提供覆盖保证.

结论:

  • DEGU提高了基因组学研究中的深度学习应用程序的可靠性.
  • 该方法为功能性基因组任务提供了可靠,可解释和不确定性意识的预测.
  • 通过量化预测不确定性,DEGU促进了基因组学可靠的决策.