提出可解释框架ILLUME+，揭示癌症药物反应中的协同基因信号。
Explainable AI for Cancer Drug Response Prediction: Beyond Univariate Feature Attributions
- 通过多维度解释机制，突破单基因重要性评估局限
- 生成更稳定的基因重要性分数，复现已知药物作用通路
- 适合肿瘤生物学家和精准医疗研究者探索新分子机制
从转录组数据预测癌症药物反应是精准肿瘤学的核心，但机器学习模型的科学价值不仅取决于预测精度，更在于能否提供可靠的生物学洞察。当前可解释性方法计算成本高、鲁棒性差，且将复杂的药物反应简化为单基因重要性评分，忽略了驱动敏感性和耐药性的协同基因活动。本文提出ILLUME+，一个可扩展的后处理可解释性框架，突破单基因评估，捕捉多种互补的解释形式。集成于端到端流程中，ILLUME+生成比现有基线更稳定的基因重要性分数，复现已知药物-基因关联与作用机制，并支持人工智能辅助假说生成，揭示癌症生物学中新型的驱动性分子信号。
原文摘要 · Abstract (English)
Predicting cancer drug response from transcriptomic profiles is a cornerstone of precision oncology, yet the scientific value of machine learning models hinges not solely on predictive accuracy, but also on their capacity to generate reliable biological insights. Current explainability approaches in this setting are computationally costly, lack robustness, and reduce complex drug response to univariate gene importance scores, overlooking the coordinated gene activity that drives sensitivity and resistance. In this work, we present ILLUME+, a scalable post-hoc explainability framework that moves beyond single-gene assessments to capture multiple, complementary forms of explanation. Integrated into our end-to-end pipeline, ILLUME+ produces more stable gene importance scores than existing baselines, recovers established drug-gene associations and mechanisms of action, and enables AI-assisted hypothesis generation to uncover novel interaction-driven molecular signals in cancer biology.
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