Biological data may be separated into primary data, such as gene expression,
and secondary data, such as pathways and protein-protein interactions. Methods
using secondary data to enhance the analysis of primary data are promising,
because secondary data have background information that is not included in
primary data. In this study, we proposed an end-to-end framework to integrally
handle secondary data to construct a classification model for primary data. We
applied this framework to cancer prognosis prediction using gene expression
data and a biological network. Cross-validation results indicated that our
model achieved higher accuracy compared with a deep neural network model
without background biological network information. Experiments conducted in
patient groups by cancer type showed improvement in ROC-area under the curve
for many groups. Visualizations of high accuracy cancer types identified
contributing genes and pathways by enrichment analysis. Known biomarkers and
novel biomarker candidates were identified through these experiments.

本研究提出了一种利用背景生物网络信息来构建分类模型的端对端框架，应用于基因表达数据和生物网络来进行癌症预后预测，实验结果表明相比于深度神经网络模型，我们的模型取得了更高的准确性，并通过富集分析鉴定了多种癌症类型的贡献基因和通路，从中识别了已知的生物标志和新的生物标志候选物。