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相关概念视频

Survival Tree01:19

Survival Tree

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Survival trees are a non-parametric method used in survival analysis to model the relationship between a set of covariates and the time until an event of interest occurs, often referred to as the "time-to-event" or "survival time." This method is particularly useful when dealing with censored data, where the event has not occurred for some individuals by the end of the study period, or when the exact time of the event is unknown.
 Building a Survival Tree
Constructing a...
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Statically Indeterminate Problem Solving01:16

Statically Indeterminate Problem Solving

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Statically indeterminate problems are those where statics alone can not determine the internal forces or reactions. Consider a structure comprising two cylindrical rods made of steel and brass. These rods are joined at point B and restrained by rigid supports at points A and C. Now, the reactions at points A and C and the deflection at point B are to be determined. This rod structure is classified as statically indeterminate as the structure has more supports than are necessary for maintaining...
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Parallel Processing01:20

Parallel Processing

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The brain processes sensory information rapidly due to parallel processing, which involves sending data across multiple neural pathways at the same time. This method allows the brain to manage various sensory qualities, such as shapes, colors, movements, and locations, all concurrently. For instance, when observing a forest landscape, the brain simultaneously processes the movement of leaves, the shapes of trees, the depth between them, and the various shades of green. This enables a quick and...
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Phylogenetic Trees03:21

Phylogenetic Trees

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Phylogenetic trees come in many forms. It matters in which sequence the organisms are arranged from the bottom to the top of the tree, but the branches can rotate at their nodes without altering the information. The lines connecting individual nodes can be straight, angled, or even curved.
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Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving01:29

Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving

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Mechanistic models play a crucial role in algorithms for numerical problem-solving, particularly in nonlinear mixed effects modeling (NMEM). These models aim to minimize specific objective functions by evaluating various parameter estimates, leading to the development of systematic algorithms. In some cases, linearization techniques approximate the model using linear equations.
In individual population analyses, different algorithms are employed, such as Cauchy's method, which uses a...
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Trial and Error and Algorithm

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A problem-solving strategy is a plan of action used to find a solution. Different strategies have distinct action plans. Trial and error involves trying different solutions until one works. For instance, to fix a broken printer, you might check ink levels, ensure the paper tray isn't jammed, and verify the printer's connection to your laptop. This method can be time-consuming but is commonly used. Thomas Edison, for example, used trial and error to find a suitable filament for the light...
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相关实验视频

Updated: Jul 6, 2025

Author Spotlight: Advancements in X-ray CT Tool Chain for Tree Core Analysis
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加快BUILD超级树算法的代应用.

Benjamin D Redelings1,2,3, Mark T Holder3,4

  • 1Biology Department, Duke University, Durham, NC, United States of America.

PeerJ
|January 8, 2024
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概括

开放生命之树 (OToL) 项目开发了一种增量算法 (BuildInc),以加快家族遗传超级树的构建. 这种新方法通过在算法调用之间共享计算工作来显著加快过程,实现高达550倍的加快速度.

关键词:
构建算法构建算法优化优化 优化优化人类遗传学 是一个学科.这是一个超级树.

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相关实验视频

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科学领域:

  • 计算生物学 计算生物学
  • 人类遗传学 是一个学科.
  • 生物信息学是一种生物信息学.

背景情况:

  • 开放生命之树 (OToL) 项目旨在综合全球的遗传学知识.
  • 超级树的构建涉及反复评估类遗传分组的兼容性,使用像Aho的Build这样的算法.
  • 现有的方法需要成千上万的Build算法调用,导致大量的计算时间.

研究的目的:

  • 描述和实施Build算法 (BuildInc) 的增量版本.
  • 为了提高开放生命之树项目的超级树建设的效率.
  • 为在软件中实现BuildInc提供实用细节.

主要方法:

  • 开发一个增量化的Build算法 (BuildInc),重复使用计算.
  • 实施细节包括伪代码和数据结构描述.
  • 使用模拟数据和现实世界的OToL合成树评估BuildInc的性能.

主要成果:

  • 通过在连续的Build调用之间共享工作,BuildInc显著减少了计算时间.
  • 性能分析表明,超级树算法的速度提高了多达550倍.
  • 增量方法对于大规模的遗传学合成是有效的.

结论:

  • 增量构建算法 (BuildInc) 提供了一种可观的性能改进,用于家族遗传超级树的构建.
  • BuildInc提高了像Open Tree of Life这样的项目的可扩展性和效率.
  • 这项工作为计算遗传学提供了有价值的优化.