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JUMPn: A Streamlined Application for Protein Co-Expression Clustering and Network Analysis in Proteomics07:28

JUMPn: A Streamlined Application for Protein Co-Expression Clustering and Network Analysis in Proteomics

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We present a systems biology tool JUMPn to perform and visualize network analysis for quantitative proteomics data, with a detailed protocol including data pre-processing, co-expression clustering, pathway enrichment, and protein-protein interaction network...
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Hierarchical and Programmable One-Pot Oligosaccharide Synthesis09:56

Hierarchical and Programmable One-Pot Oligosaccharide Synthesis

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This protocol demonstrates how to use the Auto-CHO software for hierarchical and programmable one-pot synthesis of oligosaccharides. It also describes the general procedure for RRV determination experiments and one-pot glycosylation of...
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Visualization and Quantification of High-Dimensional Cytometry Data using Cytofast and the Upstream Clustering Methods FlowSOM and Cytosplore06:01

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Cytofast is a visualization tool used to analyze output from clustering. Cytofast can be used to compare two clustering methods: FlowSOM and Cytosplore. Cytofast can rapidly generate a quantitative and qualitative overview of mass cytometry data and highlight the main differences between different clustering...
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Herein, we present a three-dimensional printing guide template for percutaneous vertebraplasty. A patient with a T11 vertebral compression fracture was selected as a case...
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Heuristic Mining of Hierarchical Genotypes and Accessory Genome Loci in Bacterial Populations08:03

Heuristic Mining of Hierarchical Genotypes and Accessory Genome Loci in Bacterial Populations

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This analytical computational platform provides practical guidance for microbiologists, ecologists, and epidemiologists interested in bacterial population genomics. Specifically, the work presented here demonstrated how to perform: i) phylogeny-guided mapping of hierarchical genotypes; ii) frequency-based analysis of genotypes; iii) kinship and clonality analyses; iv) identification of lineage differentiating accessory...
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Versatile Technique to Produce a Hierarchical Design in Nanoporous Gold

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Nanoporous gold with a hierarchical and bimodal pore size distribution can be produced by combining electrochemical and chemical dealloying. The composition of the alloy can be monitored via EDS-SEM examination as the dealloying process advances. The material's loading capacity can be determined by studying protein adsorption onto the...
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相关实验视频

Updated: Jan 20, 2026

JUMPn: A Streamlined Application for Protein Co-Expression Clustering and Network Analysis in Proteomics
07:28

JUMPn: A Streamlined Application for Protein Co-Expression Clustering and Network Analysis in Proteomics

Published on: October 19, 2021

3.6K

由等级结构引导的高维多视图聚类.

Jiajia Jiang1, Kuangnan Fang2,3, Shuangge Ma4

  • 1Department of Statistics and Data Science, College of Science, Southern University of Science and Technology, China.

Journal of multivariate analysis
|January 19, 2026
PubMed
概括
此摘要是机器生成的。

本研究引入了一种新的多视图集群方法,该方法捕获来自不同来源数据中的等级结构. 该方法有效地分析复杂的数据集,如肺癌研究中的数据集,揭示了新的见解.

关键词:
公司合并处罚 公司合并处罚层次结构 层次结构综合集群集成是一种集成集群.多视图多视图可以使用.初级 62H3030 的时间.二级 62H12 二级 62H12 是一个三级教育 62F12 三级教育

更多相关视频

Hierarchical and Programmable One-Pot Oligosaccharide Synthesis
09:56

Hierarchical and Programmable One-Pot Oligosaccharide Synthesis

Published on: September 6, 2019

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Visualization and Quantification of High-Dimensional Cytometry Data using Cytofast and the Upstream Clustering Methods FlowSOM and Cytosplore
06:01

Visualization and Quantification of High-Dimensional Cytometry Data using Cytofast and the Upstream Clustering Methods FlowSOM and Cytosplore

Published on: December 12, 2019

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相关实验视频

Last Updated: Jan 20, 2026

JUMPn: A Streamlined Application for Protein Co-Expression Clustering and Network Analysis in Proteomics
07:28

JUMPn: A Streamlined Application for Protein Co-Expression Clustering and Network Analysis in Proteomics

Published on: October 19, 2021

3.6K
Hierarchical and Programmable One-Pot Oligosaccharide Synthesis
09:56

Hierarchical and Programmable One-Pot Oligosaccharide Synthesis

Published on: September 6, 2019

7.2K
Visualization and Quantification of High-Dimensional Cytometry Data using Cytofast and the Upstream Clustering Methods FlowSOM and Cytosplore
06:01

Visualization and Quantification of High-Dimensional Cytometry Data using Cytofast and the Upstream Clustering Methods FlowSOM and Cytosplore

Published on: December 12, 2019

8.9K

科学领域:

  • 数据科学数据科学数据科学
  • 生物信息学是一种生物信息学.
  • 计算生物学 计算生物学

背景情况:

  • 多视图数据集群集成来自不同数据方面的信息.
  • 不同质的数据往往在不同的观点中表现出层次结构.
  • 现有的方法可能无法完全捕捉这些交叉视图层次关系.

研究的目的:

  • 提出一种新的高维多视图集群方法,可以考虑各视图中的等级结构.
  • 为应对多视图数据集中不同数据细分度所带来的挑战.
  • 开发一种可靠的方法来发现复杂的数据关系.

主要方法:

  • 一个新的非凸优化问题,用于分层的多视图集群.
  • 应用多变器 (ADMM) 的交替方向方法,以获得有效的解决方案.
  • 确定拟议的集群估计器的统计属性.

主要成果:

  • 拟议的方法在模拟研究中证明了有效性和优越性.
  • 它成功地确定了肺腺癌数据 (组织病理学和基因表达) 中的层次聚类结构.
  • 发现的结构与通过替代集群方法发现的结构有很大不同.

结论:

  • 新的多视图集群方法准确地捕捉了异构数据中的等级关系.
  • 这种方法为复杂的生物数据集提供了更深入的见解,例如肺癌.
  • 该方法为分析具有固有的等级结构的多模式数据提供了有价值的工具.