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相关概念视频

Hardy-Weinberg Principle01:49

Hardy-Weinberg Principle

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Diploid organisms have two alleles of each gene, one from each parent, in their somatic cells. Therefore, each individual contributes two alleles to the gene pool of the population. The gene pool of a population is the sum of every allele of all genes within that population and has some degree of variation. Genetic variation is typically expressed as a relative frequency, which is the percentage of the total population that has a given allele, genotype or phenotype.
72.1K
What is Population Genetics?01:25

What is Population Genetics?

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A population is composed of members of the same species that simultaneously live and interact in the same area. When individuals in a population breed, they pass down their genes to their offspring. Many of these genes are polymorphic, meaning that they occur in multiple variants. Such variations of a gene are referred to as alleles. The collective set of all the alleles within a population is known as the gene pool.
57.9K
Mechanistic Models: Compartment Models in Individual and Population Analysis01:23

Mechanistic Models: Compartment Models in Individual and Population Analysis

40
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...
40
Distributions to Estimate Population Parameter01:26

Distributions to Estimate Population Parameter

4.1K
The accurate values of population parameters such as population proportion, population mean, and population standard deviation (or variance) are usually unknown. These are fixed values that can only be estimated from the data collected from the samples. The estimates of each of these parameters are sample proportion, the sample mean, and sample standard deviation (or variance). To obtain the values of these sample statistics, data are required that have particular distribution and central...
4.1K
Mutation, Gene Flow, and Genetic Drift01:09

Mutation, Gene Flow, and Genetic Drift

58.4K
In a population that is not at Hardy-Weinberg equilibrium, the frequency of alleles changes over time. Therefore, any deviations from the five conditions of Hardy-Weinberg equilibrium can alter the genetic variation of a given population. Conditions that change the genetic variability of a population include mutations, natural selection, non-random mating, gene flow, and genetic drift (small population size).
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Gene Flow02:39

Gene Flow

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Gene flow is the transfer of genes among populations, resulting from either the dispersal of gametes or from the migration of individuals.
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相关实验视频

Updated: Jul 1, 2025

Predicting the Effectiveness of Population Replacement Strategy Using Mathematical Modeling
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Predicting the Effectiveness of Population Replacement Strategy Using Mathematical Modeling

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数学种群遗传学的阶段类型分布:一个新兴的框架.

Asger Hobolth1, Iker Rivas-González2, Mogens Bladt3

  • 1Department of Mathematics, Aarhus University, Denmark.

Theoretical population biology
|March 9, 2024
PubMed
概括

阶段类型分布提供了一个强大的数学框架来分析人口遗传学中的凝聚模型. 本综述解释了阶段类型理论及其用于导出统计推理祖先过程的关键性质的应用.

关键词:
燃烧的光灯.拉普拉斯变换是一个拉普拉斯变换.概率推理推理可能性推理.阶段类型理论阶段类型理论人口遗传学 人口遗传学奖励的转换是奖励的转换.

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Simultaneous Assessment of Kinship, Division Number, and Phenotype via Flow Cytometry for Hematopoietic Stem and Progenitor Cells
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Heuristic Mining of Hierarchical Genotypes and Accessory Genome Loci in Bacterial Populations
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科学领域:

  • 人口遗传学 人口遗传学
  • 数学生物学的数学生物学
  • 概率理论的概率理论是什么

背景情况:

  • 阶段类型分布模型在马尔科夫链中吸收的时间.
  • 它们为计算凝聚模型属性提供了一个一般框架.
  • 关键的凝聚过程时间和光谱是按相型分布的.

研究的目的:

  • 为了解释相型分布理论.
  • 为了证明其在导出凝聚模型属性的应用.
  • 为人口遗传学中的统计推断提供工具.

主要方法:

  • 使用矩阵操作来分析相型分布的可处理性.
  • 将凝聚模型的第一步分析与相型计算相连接.
  • 应用奖励转换来计算共差和相关性.

主要成果:

  • 阶段类型分布简化了对凝聚模型属性的计算.
  • 用相型理论推导小凝聚树的概率.
  • 用R-code展示相型框架的多功能性.

结论:

  • 阶段类型分布为理解凝聚模型提供了一个方便和多功能框架.
  • 这种方法促进了统计推断,并提供了对祖先过程的洞察力.
  • 本文所介绍的方法和R码使得可重复的分析成为可能.