Biological systems and processes are networks of complex nonlinear regulatory
interactions between nucleic acids, proteins, and metabolites. A natural way in
which to represent these interaction networks is through the use of a graph. In
this formulation, each node represents a nucleic acid, protein, or metabolite
and edges represent intermolecular interactions (inhibition, regulation,
promotion, coexpression, etc.). In this work, a novel algorithm for the
discovery of latent graph structures given experimental data is presented.

提出一种发现生物系统中潜在图结构的新算法，基于复杂的非线性调节相互作用网络，用图来表示各种核酸、蛋白质和代谢物之间的关系，并使用实验数据对其进行探测。

基因调控网络预测中的因果关系发现

Causal Discovery for Gene Regulatory Network Prediction

We propose a novel hierarchical generative model with a simple Markovian
structure and a corresponding inference model. Both the generative and
inference model are trained using the adversarial learning paradigm. We
demonstrate that the hierarchical structure supports the learning of
progressively more abstract representations as well as providing semantically
meaningful reconstructions with different levels of fidelity. Furthermore, we
show that minimizing the Jensen-Shanon divergence between the generative and
inference network is enough to minimize the reconstruction error. The resulting
semantically meaningful hierarchical latent structure discovery is exemplified
on the CelebA dataset. There, we show that the features learned by our model in
an unsupervised way outperform the best handcrafted features. Furthermore, the
extracted features remain competitive when compared to several recent deep
supervised approaches on an attribute prediction task on CelebA. Finally, we
leverage the model's inference network to achieve state-of-the-art performance
on a semi-supervised variant of the MNIST digit classification task.

提出了一种新颖的分层生成模型，使用对抗学习模型进行训练，可以逐步学习更抽象的表征，并提供具有不同保真度的语义有意义的重建。模型使用 CelebA 数据集演示了其学习的特征能够在无监督情况下胜过最佳手工特征，并在 MNIST 半监督分类任务中实现了最新的性能。