Predicting the responses of a cell under perturbations may bring important benefits to drug discovery and personalized therapeutics. In this work, we propose a novel graph variational Bayesian causal inference framework to predict a cell's gene expressions under counterfactual perturbations (perturbations that this cell did not factually receive), leveraging information representing biological knowledge in the form of gene regulatory networks (GRNs) to aid individualized cellular response predictions. Aiming at a data-adaptive GRN, we also developed an adjacency matrix updating technique for graph convolutional networks and used it to refine GRNs during pre-training, which generated more insights on gene relations and enhanced model performance. Additionally, we propose a robust estimator within our framework for the asymptotically efficient estimation of marginal perturbation effect, which is yet to be carried out in previous works. With extensive experiments, we exhibited the advantage of our approach over state-of-the-art deep learning models for individual response prediction.
翻译:在这项工作中,我们提出一个新的图表变异贝叶西亚因果推断框架,以预测一个细胞在反事实扰动(该细胞没有实际接受的扰动)下的基因表达方式下的基因表达方式,利用以基因管理网络(GRNs)形式代表生物知识的信息来帮助个人化细胞反应预测。为了对数据适应性GRN,我们还开发了一个相近矩阵,用于图层共振网络的更新技术,并在培训前改进GRNs,从而产生对基因关系的更多了解和增强模型性能。此外,我们提议在我们框架内建立一个强有力的估计器,以便对边缘扰动效应进行无干扰效应的预测,这种估计尚未在以往的工程中进行。我们通过广泛的实验,展示了我们的方法优于个人反应预测方面最先进的深学习模型。