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Propagation of Uncertainty from Random Error00:59

Propagation of Uncertainty from Random Error

645
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...
645
Random Variables01:09

Random Variables

11.4K
A random variable is a single numerical value that indicates the outcome of a procedure. The concept of random variables is fundamental to the probability theory and was introduced by a Russian mathematician, Pafnuty Chebyshev, in the mid-nineteenth century.
Uppercase letters such as X or Y denote a random variable. Lowercase letters like x or y denote the value of a random variable. If X is a random variable, then X is written in words, and x is given as a number.
For example, let X = the...
11.4K
Propagation of Uncertainty from Systematic Error01:10

Propagation of Uncertainty from Systematic Error

471
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...
471
Probability in Statistics01:14

Probability in Statistics

12.4K
Probability is the likelihood of an event occurring. The term event is defined as a collection of results of a procedure. An event is a simple event when an outcome cannot be divided into simpler parts.
An example of a simple event is a coin toss. The result of a coin toss is either a head or a tail. Here, head and tail are two simple events. These two simple events make up the sample space. Further, the probability of an event occurring falls within the range of 0 to 1. The probability of an...
12.4K
Random Error01:04

Random Error

814
Random or indeterminate errors originate from various uncontrollable variables, such as variations in environmental conditions, instrument imperfections, or the inherent variability of the phenomena being measured. Usually, these errors cannot be predicted, estimated, or characterized because their direction and magnitude often vary in magnitude and direction even during consecutive measurements. As a result, they are difficult to eliminate. However, the aggregate effect of these errors can be...
814
Probability Distributions01:32

Probability Distributions

6.8K
 The probability of a random variable x  is the likelihood of its occurrence. A probability distribution represents the probabilities of a random variable using a formula, graph, or table. There are two types of probability distribution– discrete probability distribution and continuous probability distribution.
A discrete probability distribution is a probability distribution of discrete random variables. It can be categorized into binomial probability distribution and Poisson...
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Updated: Jun 4, 2025

Determining the Likelihood of Variant Pathogenicity Using Amino Acid-level Signal-to-Noise Analysis of Genetic Variation
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Determining the Likelihood of Variant Pathogenicity Using Amino Acid-level Signal-to-Noise Analysis of Genetic Variation

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VarNMF:具有源变化的非负概率因子化.

Ela Fallik1,2, Nir Friedman1,2

  • 1School of Computer Science and Engineering, The Hebrew University of Jerusalem, Jerusalem, 9190401, Israel.

Bioinformatics (Oxford, England)
|December 28, 2024
PubMed
概括
此摘要是机器生成的。

VarNMF是一种新的概率学方法,模拟了基因组数据的源值的变化. 这种方法通过揭示患者特定的疾病行为和瘤间的变异性来增强非负矩阵因子化 (NMF).

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Basics of Multivariate Analysis in Neuroimaging Data
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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
  • 生物信息学是一种生物信息学.

背景情况:

  • 非负矩阵因子化 (NMF) 广泛用于分析混合基因组样本,例如异质组织中的细胞类型.
  • 核磁场占据了源比例和观测噪声,但在样本之间对源贡献的非微不足道变化方面扎.

研究的目的:

  • 引入VarNMF,这是NMF的一个概率扩展,旨在建模并考虑源值的变化.
  • 允许从混合样本直接恢复源变异,而无需直接观察单个源.

主要方法:

  • 瓦尔NMF将来源模型作为非负分布,扩展了标准的NMF框架.
  • 该方法应用于来自癌症和健康队列的无细胞ChIP-seq数据.

主要成果:

  • 与标准NMF相比,VarNMF提供了更好的数据分布估计.
  • 该方法成功地提取了与癌症相关的源分布,将瘤特征与贡献金额脱.
  • 瓦尔NMF识别了患者特定的疾病行为,并突出了隐藏的瘤间变异性.

结论:

  • VarNMF为NMF提供了一个强大的概率扩展,用于分析具有源变化的复杂基因组数据.
  • 该方法增强了对瘤异质性和患者特异性疾病特征的理解.