论文标题
MPVNN：突变途径可见的神经网络结构，用于可解释的癌症特异性生存风险
MPVNN: Mutated Pathway Visible Neural Network Architecture for Interpretable Prediction of Cancer-specific Survival Risk
论文作者
论文摘要
使用基因表达数据的生存风险预测对于做出癌症的治疗决策很重要。标准神经网络（NN）生存分析模型是黑匣子，缺乏解释性。更容易解释的可见神经网络（VNN）结构是使用生物途径知识设计的。但是它们没有模拟特定癌症类型的途径结构如何改变。我们提出了一种新型的突变途径VNN或MPVNN架构，该结构使用先前的信号通路知识和基因突变基于基因突变的基于数据的边缘随机模拟信号流动破坏。作为案例研究，我们使用PI3K-AKT途径，并证明了MPVNN的总体改善癌症特异性生存风险预测结果，而不是标准的非NN和其他类似尺寸的NN存活分析方法。我们表明，经过训练的MPVNN结构解释，该解释指出了PI3K-AKT途径中信号流相连的较小基因集，这些基因在特定癌症类型的风险预测中很重要。
Survival risk prediction using gene expression data is important in making treatment decisions in cancer. Standard neural network (NN) survival analysis models are black boxes with lack of interpretability. More interpretable visible neural network (VNN) architectures are designed using biological pathway knowledge. But they do not model how pathway structures can change for particular cancer types. We propose a novel Mutated Pathway VNN or MPVNN architecture, designed using prior signaling pathway knowledge and gene mutation data-based edge randomization simulating signal flow disruption. As a case study, we use the PI3K-Akt pathway and demonstrate overall improved cancer-specific survival risk prediction results of MPVNN over standard non-NN and other similar sized NN survival analysis methods. We show that trained MPVNN architecture interpretation, which points to smaller sets of genes connected by signal flow within the PI3K-Akt pathway that are important in risk prediction for particular cancer types, is reliable.
