Human-Guided Causal Knowledge Injection for Virtual Cells

Authors

Pengcheng Wang (Hunan University), Changjian Chen (Hunan University), Zhuo Tang (Hunan University), You Wu (Hunan University), Long Wang (Hunan University), Feng Yu (Hunan University), Kenli Li (Hunan University)

Presentation

Session

What does it mean to live, anyway?

Time

Wednesday, Nov 11, 10:00 – 10:12 (US/Eastern) · session 10:00 – 11:30

Location

Hall America center

Keywords

Virtual cell, causal knowledge injection, projection

Abstract

Virtual cells employ machine learning models to simulate and predict cellular behaviors, serving as a critical computational framework for investigating health and disease. Injecting causal graphs into virtual cells can improve the interpretability, but such graphs are usually not available in real-world applications. Recently, many methods have been proposed to construct causal graphs from data, which group genes based on their similarities to form concepts and extract their causal relationships. However, since this automatic process is unsupervised, the causal graphs usually contain errors. In this paper, we propose a human-guided causal knowledge injection method for virtual cells. We developed a gene-similarity-aware causal graph visualization supported by a hybrid optimization algorithm to help explore both the causal relationships between concepts and the similarities between genes. Based on the exploration, we further developed a counterfactual analysis strategy supported by a counterfactual visualization and a causal path visualization to help validate and refine causal graphs. The effectiveness of our method is demonstrated through two real-world case studies, the extraction of scientifically meaningful causal insights, and positive feedback from domain experts.

For Practitioners

Data scientists can benefit from analyzing multi-level causal relationships, while biologists can benefit from a tool for causal-based virtual cell modeling.