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使用MS1数据进行肝细胞癌诊断的深度学习框架.

Wei Xu1,2, Liying Zhang3, Xiaoliang Qian3

  • 1College of Basic Medical Science, Zhejiang Chinese Medical University, 548 Binwen Rd, Hangzhou, 310053, China.

Scientific reports
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概括

一个新的深度学习模型,MS1Former,使用原始的MS1光谱准确地分类肝细胞癌,绕过复杂的标识,以改善疾病诊断和生物标志物发现.

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科学领域:

  • 蛋白质组学是指蛋白质组学.
  • 计算生物学 计算生物学
  • 机器学习 机器学习

背景情况:

  • 临床蛋白质组学对于理解疾病机制和识别生物标志物至关重要.
  • 精确的计算疾病预测可以提高患者的诊断和预后.
  • 和蛋白质识别中的错误阻碍了蛋白质组学中的病理诊断.

研究的目的:

  • 开发一个先进的深度学习模型来分类肝细胞癌 (HCC) 和相邻的非瘤组织.
  • 为了从原始的MS1光谱进行直接分类,消除了对前体识别的需求.

主要方法:

  • 开发了一个名为MS1Former的端到端深度学习模型.
  • 使用原始的MS1光谱进行分类,重点关注微妙的m/z差异.
  • 在各种数据集上验证模型,包括数据依赖采集,数据独立采集和全扫描数据.

主要成果:

  • MS1Former准确地区分HCC瘤和相邻的非瘤组织.
  • 该模型在各种数据采集方法中展示了强大的性能.
  • 在多个外部验证数据集上实现了卓越的性能.
  • 探索了深度学习模型的可解释性.

结论:

  • MS1Former提供了一种强大而准确的方法,可以直接从MS1光谱中对HCC进行分类.
  • 端到端的框架通过删除标识步骤来简化分析管道.
  • 这种方法在临床蛋白质组学中对其他瘤类型的分类具有前景.