SLInterpreter: An Exploratory and Iterative Human-AI Collaborative System for GNN-based Synthetic Lethal Prediction
Haoran Jiang - Shanghaitech University, Shanghai, China
Shaohan Shi - ShanghaiTech University, Shanghai, China
Shuhao Zhang - ShanghaiTech University, Shanghai, China
Jie Zheng - ShanghaiTech University, Shanghai, China
Quan Li - ShanghaiTech University, Shanghai, China
Download preprint PDF
Download camera-ready PDF
Download Supplemental Material
Room: Bayshore V
2024-10-16T16:12:00ZGMT-0600Change your timezone on the schedule page
2024-10-16T16:12:00Z
Fast forward
Keywords
Synthetic Lethality, Model Interpretability, Visual Analytics, Iterative Human-AI Collaboration.
Abstract
Synthetic Lethal (SL) relationships, though rare among the vast array of gene combinations, hold substantial promise for targeted cancer therapy. Despite advancements in AI model accuracy, there is still a significant need among domain experts for interpretive paths and mechanism explorations that align better with domain-specific knowledge, particularly due to the high costs of experimentation. To address this gap, we propose an iterative Human-AI collaborative framework with 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 experts in organizing and comparing predictions and interpretive paths across different granularities, 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, enhancing expert involvement and intervention to build trust. Facilitated by SLInterpreter, this framework 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. We evaluate the framework’s efficacy through a case study and expert interviews.