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相关概念视频

Frequency-dependent Selection01:21

Frequency-dependent Selection

22.1K
When the fitness of a trait is influenced by how common it is (i.e., its frequency) relative to different traits within a population, this is referred to as frequency-dependent selection. Frequency-dependent selection may occur between species or within a single species. This type of selection can either be positive—with more common phenotypes having higher fitness—or negative, with rarer phenotypes conferring increased fitness.
22.1K
Ligand Binding and Linkage00:49

Ligand Binding and Linkage

4.8K
Allosteric proteins have more than one ligand binding site; the binding of a ligand to any of these sites influences the binding of ligands to the other sites. When a protein is allosteric, its binding sites are called coupled or linked.  In the case of enzymes, the site that binds to the substrate is known as the active site and the other site is known as the regulatory site. When a ligand binds to the regulatory site, this leads to conformational changes in the protein that can influence...
4.8K
Protein Networks02:26

Protein Networks

4.0K
An organism can have thousands of different proteins, and these proteins must cooperate to ensure the health of an organism. Proteins bind to other proteins and form complexes to carry out their functions. Many proteins interact with multiple other proteins creating a complex network of protein interactions.
These interactions can be represented through maps depicting protein-protein interaction networks, represented as nodes and edges. Nodes are circles that are representative of a protein,...
4.0K
Cytoskeletal Linker Proteins - Plakins01:09

Cytoskeletal Linker Proteins - Plakins

2.3K
Plakins are large proteins with binding domains for microtubules, microfilaments, intermediate filaments, and membrane-associated protein complexes at cell junctions. Plakin functions are evolutionarily conserved and are primarily involved in organizing the different components of the cytoskeleton by crosslinking them to each other and connecting them to the cell-matrix and cell adhesion complexes. They are also known to interact with signal transducers, serve as scaffolds for signaling...
2.3K
Protein-protein Interfaces02:04

Protein-protein Interfaces

12.5K
Many proteins form complexes to carry out their functions, making protein-protein interactions (PPIs) essential for an organism's survival. Most PPIs are stabilized by numerous weak noncovalent chemical forces. The physical shape of the interfaces determines the way two proteins interact. Many globular proteins have closely-matching shapes on their surfaces, which form a large number of weak bonds. Additionally, many PPIs occur between two helices or between a surface cleft and a...
12.5K
Friedman Two-way Analysis of Variance by Ranks01:21

Friedman Two-way Analysis of Variance by Ranks

235
Friedman's Two-Way Analysis of Variance by Ranks is a nonparametric test designed to identify differences across multiple test attempts when traditional assumptions of normality and equal variances do not apply. Unlike conventional ANOVA, which requires normally distributed data with equal variances, Friedman's test is ideal for ordinal or non-normally distributed data, making it particularly useful for analyzing dependent samples, such as matched subjects over time or repeated measures...
235

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相关实验视频

Updated: Jul 19, 2025

Author Spotlight: Impact of Intergenic Interactions on Disease-Identifying Dark Biomarkers
03:37

Author Spotlight: Impact of Intergenic Interactions on Disease-Identifying Dark Biomarkers

Published on: March 1, 2024

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在双边网络上使用矩阵分解与负样本选择进行链接预测.

Siqi Peng1, Akihiro Yamamoto1, Kimihito Ito2

  • 1Department of Intelligence Science and Technology, Graduate School of Informatics, Kyoto University, Kyoto, Japan.

PloS one
|August 16, 2023
PubMed
概括

这项研究引入了一种新的双边链路预测方法,使用负样本选择的矩阵因子分解. 我们的方法通过选择可靠的负训练样本来提高准确性,优于现有的无监督方法.

科学领域:

  • 网络科学 网络科学
  • 数据挖掘 数据挖掘
  • 机器学习 机器学习

背景情况:

  • 双边链接预测旨在识别双边网络中缺失的关系.
  • 矩阵因子化 (MF) 是一种常见的技术,但需要准确的训练数据,无论是存在的还是缺席的链接.
  • 缺乏缺失链接的基本真相对基于MF的预测构成了挑战.

研究的目的:

  • 开发一种改进的双边链接预测方法.
  • 为了应对双边网络中缺少联系的不可用实地真相的挑战.
  • 为了提高矩阵因子化用于链接预测的性能.

主要方法:

  • 提出了一种新的负样本选择技术.
  • 正式概念分析 (FCA) 用于识别可靠的负面训练样本.
  • 选择的负样本被整合到矩阵因子化 (MF) 过程中.

主要成果:

  • 拟议的联合方法显著优于基于原始MF的链接预测.
  • 实验结果表明,与现有的无监督链接预测方法相比,其性能优越.
  • 该技术在假设的应用场景中被证明是有效的.

结论:

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Using Informational Connectivity to Measure the Synchronous Emergence of fMRI Multi-voxel Information Across Time
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Using Informational Connectivity to Measure the Synchronous Emergence of fMRI Multi-voxel Information Across Time

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Author Spotlight: A Computational Approach to Decipher Amino Acid Preferences in Multispecific Protein-Protein Interactions
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Author Spotlight: A Computational Approach to Decipher Amino Acid Preferences in Multispecific Protein-Protein Interactions

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Author Spotlight: Impact of Intergenic Interactions on Disease-Identifying Dark Biomarkers

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Using Informational Connectivity to Measure the Synchronous Emergence of fMRI Multi-voxel Information Across Time
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Using Informational Connectivity to Measure the Synchronous Emergence of fMRI Multi-voxel Information Across Time

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Author Spotlight: A Computational Approach to Decipher Amino Acid Preferences in Multispecific Protein-Protein Interactions
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Author Spotlight: A Computational Approach to Decipher Amino Acid Preferences in Multispecific Protein-Protein Interactions

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  • 使用FCA进行负样本选择可以提高双边链接预测的准确性.
  • 拟议的方法为具有不完整负链路信息的网络提供了可靠的解决方案.
  • 这种方法在双边网络中推进了无监督链接预测领域.