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：预测细胞在扰动下的响应可为药物发现和个性化治疗带来重要益处。本文提出一种新颖的图变分贝叶斯因果推断框架，用于预测细胞在反事实扰动（即该细胞未实际接收的扰动）下的基因表达，通过利用以基因调控网络形式表征生物学知识的信息，辅助个体化细胞响应预测。为构建数据自适应的基因调控网络，我们还开发了适用于图卷积网络的邻接矩阵更新技术，并在预训练阶段用于优化基因调控网络，从而获得更多基因关系洞察并提升模型性能。此外，我们在框架中提出一个稳健估计量，用于边际扰动效应的渐近有效估计，这一方法此前尚未有研究涉及。通过大量实验，我们证明了该方法在个体响应预测任务上优于当前最先进的深度学习模型。