Generating realistic Multiomic data

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The problem of reverse engineering gene regulatory networks from high-throughput expression data is one of the biggestchallenges in bioinformatics. In order to benchmark network inference algorithms, simulators of well-characterizedexpression datasets are often required. However, existing simulators have been criticized because they fail to emulatekey properties of gene expression data (Maier et al., 2013). In my research project I aim to address two problems. First, Iwish to study and propose mechanisms to faithfully assess the realism of a synthetic expression dataset. Second, I wishto design an adversarial simulator of expression data based on a generative adversarial network (GAN; Goodfellow etal., 2014). This framework describes a method for estimating a generative model by playing a two-player game, in whichthe first player learns to generate samples from a particular distribution, and the second tries to discriminate them fromthe samples coming from the true data distribution. This novel deep learning framework has shown promising results fortasks such as image or audio generation, and to the best of my knowledge GANs have not yet been applied to build asimulator of gene expression data.