针对乳腺癌存活率分析的自适应性多学科整合框架
Esmaeil Hasanzadeh1, Nasrollah Moghadam Charkari2
1Faculty of Electrical and Computer Engineering, Tarbiat Modares University, Tehran, Iran.
Scientific reports
|November 3, 2025
概括
这项研究整合了使用遗传编程识别乳腺癌生物标志物的多omics数据. 该方法改善了生存分析,为癌症进展和潜在的治疗策略提供了洞察力.
科学领域:
- 在瘤学瘤学.
- 生物信息学是一种生物信息学.
- 基因组学就是基因组学.
背景情况:
- 乳腺癌是一个重大的全球健康挑战.
- 需要新的预后和治疗策略.
- 多学科数据为更深入的生物学见解提供了潜力.
研究的目的:
- 整合乳腺癌的多omics数据 (基因组学,转录组学,表观组学).
- 为了识别驱动进展和影响生存的分子特征.
- 使用遗传编程优化整合和特征选择.
主要方法:
- 利用了癌症基因组图谱 (TCGA) 的多组数据.
- 使用遗传编程进行适应性整合和特征选择.
- 开发了一个由三个组成部分组成的框架:预处理,基于GP的集成/选择和模型开发.
主要成果:
- 在培训套件上获得了78.31的一致性指数 (C指数). (5倍CV).
- 在测试组中获得了67.94的C指数.
- 通过集成的多omics证明了改善乳腺癌存活率分析.
结论:
- 适应性多omics集成显示了增强乳腺癌存活率分析的希望.
- 突出了层间分子相互作用的重要性.
- 提出了适用于其他癌症类型的灵活框架.
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