BACKGROUND Understanding the complex biological mechanisms of cancer patient survival using genomic and clinical data is vital, not only to develop new treatments for patients, but also to improve survival prediction. However, highly nonlinear and high-dimension, low-sample size (HDLSS) data cause computational challenges to applying conventional survival analysis. RESULTS We propose a novel biologically interpretable pathway-based sparse deep neural network, named Cox-PASNet, which integrates high-dimensional gene expression data and clinical data on a simple neural network architecture for survival analysis. Cox-PASNet is biologically interpretable where nodes in the neural network correspond to biological genes and pathways, while capturing the nonlinear and hierarchical effects of biological pathways associated with cancer patient survival. We also propose a heuristic optimization solution to train Cox-PASNet with HDLSS data. Cox-PASNet was intensively evaluated by comparing the predictive performance of current state-of-the-art methods on glioblastoma multiforme (GBM) and ovarian serous cystadenocarcinoma (OV) cancer. In the experiments, Cox-PASNet showed out-performance, compared to the benchmarking methods. Moreover, the neural network architecture of Cox-PASNet was biologically interpreted, and several significant prognostic factors of genes and biological pathways were identified. CONCLUSIONS Cox-PASNet models biological mechanisms in the neural network by incorporating biological pathway databases and sparse coding. The neural network of Cox-PASNet can identify nonlinear and hierarchical associations of genomic and clinical data to cancer patient survival. The open-source code of Cox-PASNet in PyTorch implemented for training, evaluation, and model interpretation is available at: https://github.com/DataX-JieHao/Cox-PASNet.

中文翻译：

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通过整合基因组和临床数据，可解释的深度神经网络可用于癌症生存分析。

背景技术利用基因组和临床数据了解癌症患者生存的复杂生物学机制至关重要，这不仅是为患者开发新的治疗方法，而且是改善生存预测的关键。但是，高度非线性和高维，低样本量（HDLSS）数据给应用常规生存分析带来了计算难题。结果我们提出了一种新的基于生物学可解释的基于路径的稀疏深度神经网络，称为Cox-PASNet，该网络将高维基因表达数据和临床数据整合在一个简单的神经网络体系结构中，以进行生存分析。Cox-PASNet具有生物学解释性，其中神经网络中的节点对应于生物学基因和途径，同时捕获了与癌症患者生存相关的生物学途径的非线性和分层效应。我们还提出了一种启发式优化解决方案，以使用HDLSS数据训练Cox-PASNet。通过比较当前最先进方法对多形性胶质母细胞瘤（GBM）和卵巢浆液性囊腺癌（OV）癌症的预测性能，对Cox-PASNet进行了深入评估。在实验中，与基准测试方法相比，Cox-PASNet表现出出色的性能。此外，对Cox-PASNet的神经网络结构进行了生物学解释，并鉴定了一些重要的基因和生物学途径的预后因素。结论Cox-PASNet通过结合生物途径数据库和稀疏编码来模拟神经网络中的生物机制。Cox-PASNet的神经网络可以识别基因组和临床数据与癌症患者生存率的非线性和层次关联。