  We train a neural network to predict human gene expression levels based on
experimental data for rat cells. The network is trained with paired human/rat
samples from the Open TG-GATES database, where paired samples were treated with
the same compound at the same dose. When evaluated on a test set of held out
compounds, the network successfully predicts human expression levels. On the
majority of the test compounds, the list of differentially expressed genes
determined from predicted expression levels agrees well with the list of
differentially expressed genes determined from actual human experimental data.
