基于建模的组织学和基因组学联合嵌入,使用正规相关性分析来预测乳腺癌存活率
Vaishnavi Subramanian1, Tanveer Syeda-Mahmood2, Minh N Do1
1Electrical and Computer Engineering, University of Illinois at Urbana-Champaign, Urbana, 61801, IL, USA.
Artificial intelligence in medicine
|March 10, 2024
概括
这项研究使用概率图形模型整合了乳腺癌组织学和基因组学数据. 新的惩罚性正统相关性分析 (pCCA) 方法提高了生存预测的准确性,并提供了可解释的结果.
科学领域:
- 计算生物学和生物信息学
- 癌症基因组学 癌症基因组学
- 医学图像分析 医学图像分析
背景情况:
- 传统的乳腺癌生存预测依赖于临床子组,PAM50基因或组织学.
- 多模式数据 (基因组学,组织学,放射学,临床) 提供了关于癌症的全面观点.
- 整合不同的数据源可以提高生存预测的准确性.
研究的目的:
- 在概率框架内明确建模多模式数据 (组织学和基因组学),用于乳腺癌存活率预测.
- 研究一种概率图形模型 (PGM) 的好处,该模型将不同数据模式视为来自同一癌症.
- 开发和验证基于嵌入方案的新型惩罚性正统相关性分析 (pCCA),以改善预测.
主要方法:
- 开发了一个概率图形模型 (PGM),整合了乳腺癌组织学和基因组学数据.
- 使用正规相关性分析 (CCA) 和其处罚变体 (pCCA) 来进行参数估计和联合嵌入生成.
- 在pCCA中引入了两个新的直角嵌入方案,以创建更丰富,多维的表示.
主要成果:
- 在具有低预测误差和信息嵌入的模拟数据上证明有效性.
- 使用TCGA组织学和RNA测序数据,实现了高达68.32%的乳腺癌生存预测平均一致性指数.
- 通过Kaplan-Meier生存分析展示了pCCA嵌入的解释性.
结论:
- 在PGM框架内明确建模多模式数据可以提高乳腺癌存活率预测.
- 处罚CCA (pCCA) 具有新的直角嵌入方案,有效地集成组织学和基因组学数据.
- 拟议的方法提供了准确和可解释的生存预测,推进了精确瘤学.
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