IEEE VIS 2024 Content: SLInterpreter: An Exploratory and Iterative Human-AI Collaborative System for GNN-based Synthetic Lethal Prediction

SLInterpreter: An Exploratory and Iterative Human-AI Collaborative System for GNN-based Synthetic Lethal Prediction

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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

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Exemplar figure, described by caption below — SLInterpreter, based on an iterative Human-AI collaboration framework, aims at 1) Human-Engaged Knowledge Graph Refinement based on Metapath Strategies and 2) Cross-Granularity SL Interpretation Enhancement and Mechanism Analysis for domain experts. Domain experts explore new SL pairs using interpretive paths generated by a model trained on the entire data. Irrelevant or incorrect paths that may introduce noise are eliminated from the KG using appropriate metapath strategies. Subsequently, the model retrains, allowing domain experts to iteratively scrutinize predictions and interpretive paths, refining the KG. This iterative process optimizes predictions and mechanism exploration, enhancing expert participation and intervention, leading to increased trust.

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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.