Predicting Cellular Responses with Variational Causal Inference and Refined Relational Information
This work addresses the challenge of individualized cellular response prediction, which is crucial for drug discovery and personalized medicine, representing an incremental advancement with novel method components.
The authors tackled the problem of predicting a cell's gene expression under counterfactual perturbations for drug discovery and personalized therapeutics, achieving improved performance over state-of-the-art deep learning models in experiments.
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.