SLInterpreter: An Exploratory and Iterative Human-AI Collaborative System for GNN-based Synthetic Lethal Prediction
Published in IEEE VIS, 2024
Synthetic Lethal (SL) relationships, although rare among the vast array of gene combinations, hold substantial promise for targeted cancer therapy. Despite advancements in AI model accuracy, there remains a persistent need among domain experts for interpretive paths and mechanism explorations that better harmonize with domain-specific knowledge, particularly due to the significant costs involved in experimentation. To address this gap, we propose an iterative Human-AI collaborative framework comprising two key components: 1)Human-Engaged Knowledge Graph Refinement based on Metapath Strategies, which leverages insights from interpretive paths and domain expertise to refine the knowledge graph through metapath strategies with appropriate granularity. 2)Cross-Granularity SL Interpretation Enhancement and Mechanism Analysis, which aids domain experts in organizing and comparing prediction results and interpretive paths across different granularities, thereby uncovering new SL relationships, enhancing result interpretation, and elucidating potential mechanisms inferred by Graph Neural Network (GNN) models. These components cyclically optimize model predictions and mechanism explorations, thereby enhancing expert involvement and intervention to build trust. This framework, facilitated by SLInterpreter, ensures that newly generated interpretive paths increasingly align with domain knowledge and adhere more closely to real-world biological principles through iterative Human-AI collaboration. Subsequently, we evaluate the efficacy of the framework through a case study and expert interviews.