Generating realistic Multiomic data

从高通量表达数据中对基因调控网络进行逆向工程是生物信息学面临的最大挑战之一。为了对网络推理算法进行基准测试，经常需要具有良好特征的表达式数据集的模拟器。然而，现有的模拟器因未能模拟基因表达数据的关键特性而受到批评(Maier等人，2013年)。在我的研究项目中，我打算解决两个问题。首先，我希望研究并提出一种机制来忠实地评估合成表达数据集的真实性。其次，我希望设计一个基于生成性对抗性网络的表情数据对抗性模拟器(GAN；GoodFloor等人，2014)。该框架描述了一种通过玩两人博弈来估计生成模型的方法，其中第一个参与者学习从特定分布生成样本，第二个参与者试图将它们与来自真实数据分布的样本区分开来。这种新的深度学习框架已经在图像或音频生成等任务中显示了良好的结果，据我所知，Gans尚未被用于构建基因表达数据的模拟器。

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.