Probabilistic Graph Pattern Matching via Tumor Knowledge Graph

Abstract

Graph Pattern Matching (GPM) entails the identification of subgraphs within a larger graph structure that either precisely mirror or closely parallel a predefined pattern graph. Despite the fact that research on GPM in large-scale graph data has been largely centered on social network analysis or enhancing the precision and efficiency of matching algorithms for expeditious subgraph retrieval, there is a noticeable absence of studies committed to probing GPM in medical domains. To rectify this shortcoming and probe the potential of GPM in clinical contexts, particularly in aiding patients with the selection of optimal tumor treatment plans, this paper introduces the concept of probabilistic graph pattern matching specifically modified for the Tumor Knowledge Graph (TKG). We propose a multi-constraint graph pattern matching algorithm, hereinafter designated as TKG-McGPM, customized for the Tumor Knowledge Graph. Through experimental verification, we establish that TKG-McGPM can facilitate more efficient and informed decision-making in tumor treatment planning.