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相关概念视频

Cancer Survival Analysis01:21

Cancer Survival Analysis

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Cancer survival analysis focuses on quantifying and interpreting the time from a key starting point, such as diagnosis or the initiation of treatment, to a specific endpoint, such as remission or death. This analysis provides critical insights into treatment effectiveness and factors that influence patient outcomes, helping to shape clinical decisions and guide prognostic evaluations. A cornerstone of oncology research, survival analysis tackles the challenges of skewed, non-normally...
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Genomics02:02

Genomics

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Genomics is the science of genomes: it is the study of all the genetic material of an organism. In humans, the genome consists of information carried in 23 pairs of chromosomes in the nucleus, as well as mitochondrial DNA. In genomics, both coding and non-coding DNA is sequenced and analyzed. Genomics allows a better understanding of all living things, their evolution, and their diversity. It has a myriad of uses: for example, to build phylogenetic trees, to improve productivity and...
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相关实验视频

Updated: Jul 12, 2025

Author Spotlight: Unveiling Transmembrane Protein Family-Related Markers in Gastric Cancer and Implications for Targeted Therapies
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Author Spotlight: Unveiling Transmembrane Protein Family-Related Markers in Gastric Cancer and Implications for Targeted Therapies

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整合泛癌多组数据,用于使用机器学习方法识别新的混合亚组.

Seema Khadirnaikar1, Sudhanshu Shukla2, S R M Prasanna1

  • 1Department of Electrical Engineering, Indian Institute of Technology Dharwad, Dharwad, Karnataka, India.

PloS one
|October 19, 2023
PubMed
概括

这项研究使用机器学习在基于分子数据的不同瘤类型中找到新的癌症患者子组. 这些新型子组表现出明显的生存率和共同的分子特征,使个性化治疗策略成为可能.

科学领域:

  • 在瘤学瘤学.
  • 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:

  • 癌症是一种复杂的疾病,具有显著的异质性,即使在来自不同器官的瘤患者中也是如此.
  • 鉴定具有相似分子特征的患者子组,无论瘤来源如何,对于开发有效的治疗策略至关重要.
  • 目前的方法往往忽略了跨不同癌症类型的共同分子变化.

研究的目的:

  • 开发一种机器学习 (ML) 管道,用于识别泛癌分析中的新型,基于多omics的患者子组.
  • 调查这些已识别的子组的临床和分子特征.
  • 创建和验证用于预测未见样本中子组成员的分类模型.

主要方法:

  • 使用ML算法从泛癌样本中获得的多奥米克数据 (mRNA,miRNA,DNA甲基化,蛋白质表达) 的连锁和非线性维度减少.
  • 预测数据的聚类,以识别基于多个omics的新型子组.
  • 分组的临床表征,包括总生存率 (OS) 和无病生存率 (DFS) 分析.
  • 开发和验证决策级别和特征级别的融合分类模型,用于分组识别.

主要成果:

  • 在胰腺癌数据中识别ML衍生的新型患者亚组.
  • 在已识别的子组中观察到总生存 (OS) 和无病生存 (DFS) 的显著差异 (p值<0.0001).

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Performing Data Mining And Integrative Analysis Of Biomarker in Breast Cancer Using Multiple Publicly Accessible Databases
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  • 小组包括来自不同瘤类型的患者,但具有相似的分子变化,包括免疫微环境,突变配置文件和丰富的途径.
  • 经过验证的分类模型准确地将类标签分配给验证样本,确认子组的分子特征.

    • 结论:

    • 在全癌症分析中,可以使用机器学习识别基于多omics的新型患者子组.
    • 患有不同瘤类型的患者可以表现出类似的分子特征,挑战传统的器官特定分类.
    • 开发的分类模型对于识别这些新型子组是有效的,并且可以根据子组特定的分子形状来设计定制的治疗方案.