  With the rapid development of high-throughput experimental technologies,
different types of omics (e.g., genomics, epigenomics, transcriptomics,
proteomics, and metabolomics) data can be produced from clinical samples. The
correlations between different omics types attracts a lot of research interest,
whereas the stduy on genome-wide omcis data translation (i.e, generation and
prediction of one type of omics data from another type of omics data) is almost
blank. Generative adversarial networks and the variants are one of the most
state-of-the-art deep learning technologies, which have shown great success in
image-to-image translation, text-to-image translation, etc. Here we proposed
OmiTrans, a deep learning framework adopted the idea of generative adversarial
networks to achieve omics-to-omics translation with promising results. OmiTrans
was able to faithfully reconstruct gene expression profiles from DNA
methylation data with high accuracy and great model generalisation, as
demonstrated in the experiments.
