  A unique challenge in predictive model building for omics data has been the
small number of samples $(n)$ versus the large amount of features $(p)$. This
"$n\ll p$" property brings difficulties for disease outcome classification
using deep learning techniques. Sparse learning by incorporating external gene
network information such as the graph-embedded deep feedforward network (GEDFN)
model has been a solution to this issue. However, such methods require an
existing feature graph, and potential mis-specification of the feature graph
can be harmful on classification and feature selection. To address this
limitation and develop a robust classification model without relying on
external knowledge, we propose a \underline{for}est
\underline{g}raph-\underline{e}mbedded deep feedforward \underline{net}work
(forgeNet) model, to integrate the GEDFN architecture with a forest feature
graph extractor, so that the feature graph can be learned in a supervised
manner and specifically constructed for a given prediction task. To validate
the method's capability, we experimented the forgeNet model with both synthetic
and real datasets. The resulting high classification accuracy suggests that the
method is a valuable addition to sparse deep learning models for omics data.
