交叉注意力可以在有限的OMIC-成像-临床数据上进行深度学习,这些数据来自130名肺癌患者
Suraj Verma1, Giuseppe Magazzù2, Noushin Eftekhari3
1School of Computing, Engineering and Digital Technologies, Teesside University, Middlesbrough, UK.
Cell reports methods
|July 9, 2024
概括
深度学习模型使用多omics和成像数据预测非小细胞肺癌 (NSCLC) 的生存率. 一种新的交叉注意力方法提高了准确性,从有限的患者样本中识别了关键生物标志物.
科学领域:
- 在瘤学瘤学.
- 生物信息学是一种生物信息学.
- 医疗成像医学成像
背景情况:
- 深度学习使用多omics数据推进了生存预测.
- 整合omics,成像和临床数据存在挑战,因为数据集的大小很小.
研究的目的:
- 开发生物可解释的深度学习模型,用于非小细胞肺癌 (NSCLC) 的生存预测.
- 整合多omics数据,CT扫描和临床信息,以便进行可靠的预后因素提取.
主要方法:
- 为NSCLC生存分析提出了两个深度学习架构.
- 综合患者特定的临床,转录和成像数据.
- 整合了KEGG和Reactome路径信息,以提高生物解释性.
主要成果:
- 两种模型都精确地将NSCLC患者分为高风险和低风险组,使用一个小数据集 (130名患者).
- 稀疏的自动编码器中的交叉注意力机制显著改善了模型性能.
- 确定了瘤区域和NSCLC相关基因作为潜在的预后生物标志物.
结论:
- 拟议的深度学习模型为使用小型成像-omics-临床数据集的生存预测提供了显著的方法进步.
- 交叉注意力机制增强了关键生物标志物的识别,并提高了NSCLC的预后准确性.
- 生物知情深度学习为个性化癌症生存预测提供了希望.
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