面向虚拟细胞的人导式因果知识注入

中文摘要 AI 辅助

虚拟细胞采用机器学习模型模拟和预测细胞行为，是研究健康与疾病的关键计算框架。向虚拟细胞中注入因果图可提升其可解释性，但实际应用中通常无法获取这类因果图。近来，已有诸多方法被提出用于从数据中构建因果图，这些方法基于基因的相似性对其分组形成概念，并提取概念间的因果关系。不过，由于该自动过程是无监督的，生成的因果图通常存在错误。本文提出一种面向虚拟细胞的人导式因果知识注入方法：开发了基因感知相似性的因果图可视化，结合混合优化算法，以辅助探索概念间的因果关系与基因间的相似性；在该探索基础上，进一步开发了反事实分析策略，辅以反事实可视化与因果路径可视化，用于验证和优化因果图。通过两个实际案例研究、提取具有科学意义的因果见解，以及领域专家的积极反馈，证明了所提方法的有效性。

英文摘要

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.

发表机构

• Hunan University（湖南大学）
• Yuelushan Laboratory（岳麓山实验室）

机构由 AI 辅助整理，请以论文原文为准。