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相关概念视频

Cluster Sampling Method01:20

Cluster Sampling Method

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Appropriate sampling methods ensure that samples are drawn without bias and accurately represent the population. Because measuring the entire population in a study is not practical, researchers use samples to represent the population of interest.
To choose a cluster sample, divide the population into clusters (groups) and then randomly select some of the clusters. All the members from these clusters are in the cluster sample. For example, if you randomly sample four departments from your...
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相关实验视频

Updated: Jan 9, 2026

ExCYT: A Graphical User Interface for Streamlining Analysis of High-Dimensional Cytometry Data
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使用图形网络技术的放射性聚类与不平衡的最佳运输技术相结合.

Jung Hun Oh1, Aditya Apte1, Harini Veeraraghavan1

  • 1Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, NY, USA.

Computational and structural biotechnology journal
|December 8, 2025
PubMed
概括

这项研究引入了一种新的网络模型和集群算法,用于从头部和部状细胞癌 (HNSCC) 和非小细胞肺癌 (NSCLC) 的放射性数据中识别患者子组. 这些发现揭示了与生存结果和瘤免疫相互作用相关的独特放射型.

关键词:
网络竞技 网络竞技网络分析 网络分析最佳的运输方式无线电学 (Radiomics) 是一种辐射学.样本聚类是指样本的聚类.

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Large-scale Reconstructions and Independent, Unbiased Clustering Based on Morphological Metrics to Classify Neurons in Selective Populations
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JUMPn: A Streamlined Application for Protein Co-Expression Clustering and Network Analysis in Proteomics
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科学领域:

  • 在瘤学瘤学.
  • 放射学 放射学是一门学科.
  • 生物信息学是一种生物信息学.
  • 机器学习 机器学习

背景情况:

  • 高维的放射性数据集往往需要分组识别才能有效地应用机器学习.
  • 有限的数据集大小给癌症研究中的传统机器学习方法带来了挑战.

研究的目的:

  • 开发和验证一种用于识别癌症数据集中的放射性子组的新方法.
  • 调查头部和部平细胞癌 (HNSCC) 和非小细胞肺癌 (NSCLC) 中确定的放射性型的预后价值和免疫细胞关联.

主要方法:

  • 为了识别子网络,使用了具有扩展贝叶斯信息标准的规范化网络模型.
  • 一个基于图形网络的k-means集群算法与不平衡的最佳运输被开发用于样本分组.
  • 对已识别的子组进行了生存分析 (Kaplan-Meier) 和CIBERSORT分析,使用CT扫描和RNA-Seq数据的放射性特征.

主要成果:

  • 拟议的方法确定了HNSCC中高风险和低风险组,显示了无进展生存率的显著差异 (p=0.0202).
  • 在NSCLC中,不同的高风险和低风险组在整体存活率 (p=0.0007) 中存在显著差异.
  • 在风险组之间观察到免疫细胞丰度的显著差异 (HNSCC中中性粒细胞;NSCLC中静止的树突细胞和激活的母细胞).

结论:

  • 放射性特征可以有效地识别具有明显预后的放射性表型.
  • 鉴定出的放射类型可能与不同的瘤免疫相互作用有关,为个性化癌症治疗策略提供了潜在的潜力.