Modelling Metabolism Pathways using Graph Representation Learning for Fraud Detection in Sports

Modelling biological pathway plays an important role in understanding different processes for decision making, especially in forensic investigations on doping activities in sports. Recently, the issue of sample swapping has arisen as a potential fraudulent behaviour by athletes to avoid a positive doping test result. The current detection models neglect an important factor, i.e., leveraging the steroid metabolism pathway of the human body. The spatial relationships between different metabolites within the steroid metabolism pathways are important and cannot be merely treated as linear correlations when assessing similarities among the samples obtained from athletes. To address this challenge, we propose the GRAMP model based on graph representation learning to incorporate domain knowledge into the model decision for the detection of sample swapping. Our model takes into account the spatial structural dependencies of different metabolites using a graph attention mechanism and generates high-level embeddings to detect fraudulent behaviour. We evaluate our approach through extensive experiments on real-world datasets and find that our proposed model outperforms existing state-of-the-art models for fraud detection tasks in sports, demonstrating the effectiveness of our approach and its potential impact on decision making.