Modelling Metabolism Pathways using Graph Representation Learning for Fraud Detection in Sports
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".