从黑盒子到生物洞察力:注意力开启了肺癌中的多奥米克动态
Yuhang Huang1, Yungang He2, Liyan Zeng2
1Institutes of Biomedical Sciences, Fudan University, 131 Dongan Road, Shanghai 200032, China.
Cancers
|March 14, 2026
概括
通过整合多omics数据,AttentioFuse提高了非小细胞肺癌 (NSCLC) 的预后. 这种可解释的深度学习模型为精密疗法提供了生物学基础的见解,改善了临床决策.
科学领域:
- 计算生物学是一种计算生物学.
- 基因组学就是基因组学.
- 机器学习在瘤学中
背景情况:
- 非小细胞肺癌 (NSCLC) 亚型,肺腺癌 (LUAD) 和状细胞癌 (LUSC),具有不同的分子形状,影响预后和治疗.
- 深度学习模型为NSCLC提供了高预测准确度,但缺乏透明度,阻碍了临床采用.
研究的目的:
- 开发AttentioFuse,这是一个可解释的深度学习框架,用于在LUAD和LUSC中准确的预后和治疗.
- 通过使用Reactome指导的策略来整合多omics数据,以提高生物洞察力.
主要方法:
- AttentioFuse利用双相学习与欧米特异性编码器和层次注意力机制来进行动态层贡献分析.
- 综合解释性结合了DeepSHAP和全球关注权重,用于基因到通路的解释.
- 两种变体,AtentioFuse-3F和AtentioFuse-5X,被评估为歧视和等级解释.
主要成果:
- 在TCGA LUAD/LUSC队列上,AttentioFuse实现了最先进的TNM分期性能.
- 确定了关键的生物学见解,包括泛NSCLC AKT/mTOR代谢调节和组织学特定的Notch信号.
- 发现了与发育活性,微生物群相关转移和细胞外矩阵重塑相关的途径.
结论:
- AttentioFuse-5X成功地平衡了预测准确性与层次的路径级解释.
- 该框架将黑子深度学习预测转化为可操作的,生物知情的瘤学决策支持.
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