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The identification of cancer-specific biomarkers and therapeutic targets is one of the primary goals of cancer genomics. Thousands of cancer genomes, exomes, and transcriptomes have been sequenced to date. In this study, we conducted a pan-cancer analysis of transcriptome datasets from 37 cancer types provided by The Cancer Genome Atlas (TCGA) in an effort to identify cancer-specific gene expression signatures. We employed deep neural networks to train a model on the transcriptome profiledoi:10.1101/2021.03.15.435283 fatcat:stasu6xmm5hv3m55mttrtjpwcy