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相关概念视频

Long-term Potentiation01:35

Long-term Potentiation

54.8K
Long-term potentiation, or LTP, is one of the ways by which synaptic plasticity—changes in the strength of chemical synapses—can occur in the brain. LTP is the process of synaptic strengthening that occurs over time between pre- and postsynaptic neuronal connections. The synaptic strengthening of LTP works in opposition to the synaptic weakening of long-term depression (LTD) and together are the main mechanisms that underlie learning and memory.
54.8K
Signal Flow Graphs01:18

Signal Flow Graphs

176
Signal-flow graphs offer a streamlined and intuitive approach to representing control systems, providing an alternative to traditional block diagrams. These graphs use branches to symbolize systems and nodes to represent signals, effectively illustrating the relationships and interactions within the system.
In a signal-flow graph, branches denote the system's transfer functions, while nodes represent the signals. The direction of signal flow is indicated by arrows, with the corresponding...
176
SFG Algebra01:16

SFG Algebra

107
In Signal Flow Graph (SFG) algebra, the value a node represents is determined by the sum of all signals entering that node. This summed value is then transmitted through every branch leaving the node, making the SFG a powerful tool for visualizing and analyzing control systems.
Each node in an SFG corresponds to a variable, and the interactions between nodes are represented by branches with associated gains. When multiple branches lead into a node, the value at that node is the sum of the...
107
Mnemonic Devices01:23

Mnemonic Devices

58
Mnemonic devices are cognitive tools that facilitate memory retention by linking new information to familiar patterns or organizational strategies. These techniques are beneficial for remembering complex or lengthy sets of information by simplifying and structuring them in easily retrievable ways.
Acronyms
Acronyms are created by using the initial letters of a series of words to form a new word or phrase. This approach condenses complex information into a single, memorable entity. For example,...
58

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Time-lapse Live Imaging and Quantification of Fast Dendritic Branch Dynamics in Developing Drosophila Neurons
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SEMdag:通过节点或层次排序来快速学习定向环形图.

Mario Grassi1, Barbara Tarantino1

  • 1Department of Brain and Behavioral Sciences, University of Pavia, Pavia, Italy.

PloS one
|January 8, 2025
PubMed
概括

通过两步的方法,SEMdag () 从高维数据中高效地学习因果结构. 它准确地预测疾病,优于现有方法,特别是使用自下而上的方法的稀疏数据.

科学领域:

  • 因果推理和图形模型.
  • 生物信息学和计算生物学
  • 统计遗传学 统计遗传学

背景情况:

  • 定向环形图 (DAG) 对于表示变量之间的因果关系至关重要.
  • 从观测数据中学习DAG结构是各种科学领域的一个重大挑战.
  • 现有的因果发现方法经常与高维数据集扎.

研究的目的:

  • 介绍SEMdag (),一种新的两步方法,用于学习高维线性结构方程模型 (SEM).
  • 评估SEMdag在恢复可信的DAG和预测疾病结果方面的表现.
  • 通过使用现实世界的生物数据,与已建立的因果发现技术进行SEMdag的比较.

主要方法:

  • SEMdag () 采用基于顺序的双阶段搜索,使用先前知识 (基于知识的,KB) 或数据驱动 (自下而上的,BU) 策略.
  • 该方法假设线性SEM具有相同的差异误差值.
  • 用四种疾病 (ALS,BRCA,COVID-19,STEMI) 的RNA-seq数据评估性能,并使用随机森林 (RF) 评估疾病预测.

主要成果:

  • SEMdag ((() 成功恢复了显示强大的疾病预测性能的图形结构.
  • 下向 (BU) 方法在稀疏的初始图表中显示出更好的结果,而BU和基于知识 (KB) 在更密集的图表上表现良好.

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  • KB方法,特别是在利用拓层时,获得了最高的预测分数.
  • 结论:

    • 对于高维因果结构学习,SEMdag提供了一个计算效率高,灵活的工具.
    • 该方法与现有的因果发现技术相比,提供了优越的疾病预测能力.
    • SEMdag () 在R包SEMgraph中实现,促进其在研究中的可访问性和应用.