Human-Guided Causal Knowledge Injection for Virtual Cells
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 ...
| Authors | Pengcheng Wang, Changjian Chen, Zhuo Tang et al. |
|---|---|
| Primary category | cs.HC |
| Published | 9 August 2026 |
| Last updated at source | 9 August 2026 |
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