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Cancers Originate from Somatic Mutations in a Single Cell02:21

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Cancer arises from mutations in the critical genes that allow healthy cells to escape cell cycle regulation and acquire the ability to proliferate indefinitely. Though originating from a single mutation event in one of the originator cells, cancer progresses when the mutant cell lines continue to gain more and more mutations, and finally, become malignant. For example, chronic myelogenous leukemia (CML) develops initially as a non-lethal increase in white blood cells, which progressively...
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Comparative Lesions Analysis Through a Targeted Sequencing Approach
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DeepGene-BC:通过体位点突变档案进行基于深度学习的乳腺癌亚型预测.

Pengfei Hou1,2,3,4, Liangjie Liu1,2, Yijia Duan1,2

  • 1Bio-X Institutes, Key Laboratory for the Genetics of Developmental and Neuropsychiatric Disorders, Shanghai Jiao Tong University, Shanghai 200030, China.

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概括
此摘要是机器生成的。

DeepGene-BC是一个新的深度学习框架,使用基因组突变来准确地分类乳腺癌. 这种方法提供了一个强大的替代方案,用于精确瘤学的转录组形状.

关键词:
乳腺癌 乳腺癌 乳腺癌癌症亚型 癌症亚型深度学习是一种深度学习.身体突变是一种体质突变.

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科学领域:

  • 基因组学就是基因组学.
  • 计算生物学 计算生物学
  • 在瘤学瘤学.

背景情况:

  • 传统的乳腺癌分子亚型依赖于转录基因特征,在强度和临床使用方面面临挑战.
  • 体点突变提供了一个稳定的基因组替代方案,但也带来了诸如高维度和稀疏性等挑战.
  • 现有的方法很难利用稀疏的基因组突变数据的全部预测潜力.

研究的目的:

  • 开发一个深度学习框架,deepGene-BC,使用基因组突变数据进行精确的乳腺癌分子亚型化.
  • 克服高维度和突变数据稀疏性的局限性,用于预测建模.
  • 整合路径信息和先进的深度学习技术,以改进子类型.

主要方法:

  • 开发了deepGene-BC,这是一个结合路径信息特征选择和混合神经网络的深度学习框架.
  • 采用突变复发过,路径先验,以及用于特征改进的相互信息.
  • 利用专门的混合神经网络架构在稀疏数据中建模线性,交互性和非线性模式.

主要成果:

  • 在独立的TCGA乳腺癌队列中,DeepGene-BC实现了77.3%的整体准确率和75.2%的平均灵敏度.
  • 显示出强大的歧视性表现,宏观平均AU-ROC为0.94 (95%CI:0.92-0.96).
  • 该框架有效地将全基因组突变提炼成一个紧的,可解释的特征集.

结论:

  • DeepGene-BC成功地将生物信息特征工程与乳腺癌分层的深度学习相结合.
  • 该框架显示了对非侵入性分子亚型和推进精确瘤学的重大前景.
  • 这种方法为乳腺癌亚型识别提供了一种强大且可能更具临床适用性的方法.