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通过学习相关性最大化表示来深入的多omics集成可以识别预后分层的癌症亚型.

Yanrong Ji1, Pratik Dutta2, Ramana Davuluri2

  • 1Division of Health and Biomedical Informatics, Department of Preventive Medicine, Northwestern University Feinberg School of Medicine, Chicago, IL 60611, USA.

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概括

我们开发了DeepMOIS-MC (Deep Multi-Omics Integrative Subtyping by Maximizing Correlation),这是一个用于分子子类型的新框架. 这种方法通过整合多omics数据来改善患者分层,在癌症研究中表现优于传统方法.

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科学领域:

  • 计算生物学和生物信息学
  • 基因组学和多基因组学数据整合.
  • 精准医学和癌症研究.

背景情况:

  • 分子亚型对精密医学至关重要,它可以识别临床可行的疾病亚组.
  • 整合多学科数据为强大的疾病分层提供了全面的观点.
  • 现有的方法可能无法充分利用各种omics数据集内部和跨越各种数据集的相关性.

研究的目的:

  • 开发一种新的以结果为导向的分子分组框架,即通过最大化相关性 (DeepMOIS-MC) 来进行深度多基因整合分类.
  • 通过最大限度地提高所有输入omics视图之间的相关性来增强来自多omics数据的整合性学习.
  • 改善患者分层,用于癌症的精准医学应用.

主要方法:

  • DeepMOIS-MC采用了一个由两个部分组成的框架:聚类和分类.
  • 聚类利用双层神经网络和通用法定相关性分析损失来进行共享表示学习,随后是以结果为导向的特征选择和聚类.
  • 分类涉及特征选择 (RandomForest) 和预测建模 (XGBoost) 在离散的奥米克数据上,以预测已识别的分子子组.

主要成果:

  • DeepMOIS-MC应用于肺癌和肝癌TCGA数据集,与传统方法相比,显示出优越的患者分层.
  • 对比分析证实了DeepMOIS-MC在识别强大的分子子组中的有效性.
  • 分类模型在独立数据集上显示出强度和通用性.

结论:

  • DeepMOIS-MC提供了一个强大的和适应性的框架,用于多主题的综合分析.
  • 该方法有助于发现临床相关的分子子组,进步精确瘤学.
  • 深度MOIS-MC具有在各种多主题整合任务中广泛采用的潜力.