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自我规范化的多态神经网络用于全癌症预测.

Asim Waqas1,2,3, Aakash Tripathi2,3, Sabeen Ahmed2,3

  • 1Department of Cancer Epidemiology, Moffitt Cancer Center and Research Institute, Tampa, FL 33612, USA.

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概括

SeNMo是一种新的深度学习模型,集成多omics数据来预测癌症存活率和分类瘤类型,即使缺少患者数据. 这种方法提高了瘤学的诊断和预后能力.

关键词:
癌症 癌症 癌症 癌症 癌症这是分类分类的分类.深度学习是一种深度学习.机器学习是机器学习.多种主题的多种主题.这是一个多式联络模式.瘤学 在瘤学方面.胰腺癌是一种癌症.幸存率 幸存率 生存率

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科学领域:

  • 计算生物学和生物信息学
  • 癌症基因组学和精确瘤学
  • 机器学习在医疗保健中的应用

背景情况:

  • 从稀疏的,高维的多维数据中提取预后 (总生存率,三级淋巴细胞结构比率) 和诊断 (癌症类型) 签名是具有挑战性的,因为异质性和缺失.
  • 现有的方法很难有效地整合多种omics数据 (基因表达,甲基化,miRNA,突变,蛋白质) 和临床变量.
  • 需要强大的模型,能够处理缺失的数据模式,以便进行全面的癌症分析.

研究的目的:

  • 开发和验证SeNMo,一个自我规范化的深度神经网络,用于从异质的奥米克数据中学习统一的表示.
  • 证明SeNMo在预测整体存活率,分类原发性癌症类型和预测三级淋巴体结构比率方面的能力.
  • 建立一个模式-稳固的基线模型,用于多主题瘤学的多种下游任务.

主要方法:

  • 在五个奥米克层 (基因表达,DNA甲基化,miRNA,体突变,蛋白质表达) 和临床变量上训练了一种自我正常化的深度神经网络 (SeNMo).
  • 利用来自癌症基因组图谱 (TCGA) 的10,000多个患者个人资料进行培训和内部验证.
  • 在CPTAC肺状细胞癌队列和独立的莫菲特癌症中心队列上进行了外部验证.

主要成果:

  • 在一个持有的TCGA测试集上,实现了0.758的整体生存预测的一致性指数.
  • 外部验证显示一致性指数为0.73 (CPTAC) 和0.66 (莫菲特).
  • 分类初级癌症类型的准确率为99.8%,预测的三级淋巴体结构比率与专家注释一致 (p < 0.05),并显示出明显的生存结果.

结论:

  • SeNMo提供了一个强大的,模态无关的深度学习框架,用于整合瘤学中的多omics数据.
  • 该模型在关键临床任务中表现出强的表现,包括生存预测,癌症分类和三级淋巴细胞结构比率预测.
  • 通过全面的多学科分析,SeNMo具有重要的翻译潜力,可以通过全面的多学科分析来推进精密瘤学.