Implementation of a method based on a conditional generative adversarial network to generate realistic transcriptomics data for E. coli and humans. The synthetic data preserves tissue and cancer-specific properties of transcriptomics data.

Keywords: bioinformatics, E. coli, gene expression, gene regulatory network, adversarial networks, machine learning, Python, TensorFlow