An end-to-end framework for gene expression classification by
  integrating a background knowledge graph: application to cancer prognosis
  prediction
Kazuma Inoue Ryosuke Kojima Mayumi Kamada Yasushi Okuno
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.