用可解释的深度学习模型揭示基因组改变对癌细胞信号传递的影响
Jonathan D Young1, Shuangxia Ren1, Lujia Chen2
1Intelligent Systems Program, School of Computing and Information, University of Pittsburgh, Pittsburgh, PA 15260, USA.
Cancers
|August 12, 2023
概括
这项研究引入了一种深度学习模型,即冗余输入神经网络 (RINN),以了解致癌的基因组变化如何影响细胞信号和基因表达. RINN准确地预测基因表达,并揭示基因组变化和信号通路之间的功能联系.
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.
- 癌症研究 癌症研究
背景情况:
- 癌症是由体基因组改变 (SGA) 驱动的异常细胞信号引起的.
- 瘤异质性导致独特的信号系统中断.
- 了解这些复杂的信号网络对于癌症研究至关重要.
研究的目的:
- 开发一种计算模型,将癌症信号系统解释为因果图形模型.
- 利用深度学习来预测由SGA引起的基因表达变化.
- 确定SGA对共同信号通路的共享功能影响.
主要方法:
- 开发了一种名为冗余输入神经网络 (RINN) 的新型深度学习模型.
- 使用SGA作为RINN模型的直接输入.
- 采用因果图形模型来表示癌症信号通路.
- 评估模型性能使用ROC曲线下的面积用于基因表达预测.
主要成果:
- 在RINN模型准确地预测基因表达基于SGA输入.
- 该模型展示了编码SGA对细胞信号传输的影响的能力.
- RINN成功地确定了SGAs影响PI3K,Nrf2和TGF等常见途径的共享功能影响.
- 该模型可以重新发现细胞信号系统中的已知关系.
结论:
- RINN模型为分析癌症信号网络提供了一个透明和有效的工具.
- 这种方法增强了我们对基因组改变如何推动癌症进展的理解.
- 通过阐明信号通路失调,RINN模型有可能揭示新的治疗点.
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