Title: Towards Gene Expression Convolutions using Gene Interaction Graphs,
Abstract: We study the challenges of applying deep learning to gene expression data. We
find experimentally that there exists non-linear signal in the data, however is
it not discovered automatically given the noise and low numbers of samples used
in most research. We discuss how gene interaction graphs (same pathway,
protein-protein, co-expression, or research paper text association) can be used
to impose a bias on a deep model similar to the spatial bias imposed by
convolutions on an image. We explore the usage of Graph Convolutional Neural
Networks coupled with dropout and gene embeddings to utilize the graph
information. We find this approach provides an advantage for particular tasks
in a low data regime but is very dependent on the quality of the graph used. We
conclude that more work should be done in this direction. We design experiments
that show why existing methods fail to capture signal that is present in the
data when features are added which clearly isolates the problem that needs to
be addressed.